{"as_of":"2026-08-05T02:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:036efb8f0f826e9a08784d8587fa49bb80ef2638575badbeb7f4cf298f9087b8","coverage":[{"denominator":201,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T00:56:13.855741Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T14:05:53.003364Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.08474","snapshot_observed_at":"2026-08-02T14:05:53.003364Z","title":"arXiv:2605.08474v1https://arxiv.org/pdf/2605.08474v1","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22551","last_updated":"2026-05-14T11:05:09Z","snapshot_observed_at":"2026-08-02T14:05:49.199892Z","submitted_at":"2026-05-14T11:05:09Z","title":"Mirror Descent Methods for Quasar Convex Optimization Problems With Non-Smooth Inequality Constraints","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T14:05:53.003364Z"},"links":{"cited_paper":"/paper/2605.08474","citing_paper":"/paper/2607.22551"},"observation_digest":"sha256:b1459066c4d418448f5c3a896ee582783e3ea59096691e0664126c85717f7bd0","observation_id":"f87828c3-4401-4468-97fd-aaf9bd71b980","resolution":{"observed_at":"2026-08-02T14:05:53.003364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2605.08474/citation-record","integrity":"/paper/2605.08474/integrity","json":"/paper/2605.08474/citation-record.json","paper":"/paper/2605.08474"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1494567d-a148-47e6-9d5c-a9252a113914","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:3ea575d37d7310cdc80c95b4041feed000e856c3ec3585181ee94d92cbdb9911","observation_id":"8f9701a7-6812-4c3c-a61e-35d83db3dea3","resolution":{"observed_at":"2026-05-14T09:17:58.409902Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , volume =","venue":null,"work_id":"8d31d21b-b644-4f72-bff9-f725f3abfe18","year":2024},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:f37608e19a45e9e839abe10580d23dd5b64908e1661cdde58b619b06b71ee7fd","observation_id":"43d5d90f-aeb8-40f5-aaef-e15dd820d3d5","resolution":{"observed_at":"2026-05-14T09:17:58.189033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Submitted manuscript , year =","venue":null,"work_id":"45b12bf7-1e56-4a21-8bf1-d45c2cc163b8","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:28c5e4356f9e0b2f37e52a9985a2ffea17a32b3eb6b03dfc2e887a379f2c80f2","observation_id":"8e51400e-5a6a-4e9c-ba16-11c103b1c84c","resolution":{"observed_at":"2026-05-14T09:17:58.200219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Optimization , volume =","venue":null,"work_id":"21b7e73f-d565-4680-9ed7-a6033a3c3cfb","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:e2a535b8f407a6e3e71fea2fc7d86bc9c928dde2be2ee012fd4dc06591b8a6d6","observation_id":"f36e5430-83d1-4139-a974-0fbaa32b7de9","resolution":{"observed_at":"2026-05-14T09:17:58.195628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On efficiency of nonmonotone","venue":null,"work_id":"7c277080-1270-4444-9cdd-6af731f46f72","year":2017},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:2d79569ce5657b963b925948de2b56af6300dfb50ca2de9d4de3dbca506b6fcf","observation_id":"d7756e36-9d83-4a13-bae2-530c0ca2b7e6","resolution":{"observed_at":"2026-05-14T09:17:58.205213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Methods of Operations Research , volume=","venue":null,"work_id":"df7d1bb2-00a8-47cc-9eb1-3e4b88485203","year":2019},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:601d83bd8080323c1dae555b1dc125590d8acaf39ecd07bb047f490a9655799a","observation_id":"810f427a-4059-403f-bc52-aa417594567d","resolution":{"observed_at":"2026-05-14T09:17:58.192399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.20484","last_updated":"2026-07-29T15:55:23Z","snapshot_observed_at":"2026-08-01T23:31:47.026366Z","submitted_at":"2025-05-26T19:41:52Z","title":"Asymptotic Convergence Analysis of High-Order Proximal-Point Methods Beyond Sublinear Rates","version":2},"cited_work":{"arxiv_id":"2505.20484","doi":"10.48550/arxiv.2505.20484","metadata_source":"arxiv_reference","pith_arxiv_id":"2505.20484","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ahookhosh, A","venue":"ArXiv.org","work_id":"1348533b-ec44-4e67-be69-985bbf5c26bf","year":2025},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"cited_paper":"/paper/2505.20484","citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:531c2039ee957dc359bb76cbd9f798da6e78bb0f865002f665b24642baa11616","observation_id":"36b0b21f-25f1-4f4f-89e5-c884f6539f92","resolution":{"observed_at":"2026-07-30T01:18:54.772655Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and de Brito, J","venue":null,"work_id":"254adad1-7019-4620-9dd5-33aca1f08381","year":2026},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:847e90971a2ef77d021fa832dc0b18b9f11166ea0ef1a6fa2091b412c3c65451","observation_id":"b7382ca8-e5ad-4e33-8052-6d1b6e31ce6b","resolution":{"observed_at":"2026-05-14T09:17:58.193819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Hadjisavvas, N","venue":null,"work_id":"865bca42-97e8-496a-944e-87ba42077b36","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:db7809de316cce4258cc869f0ca8ac0dfbb06fda666fabf719335a59806e789d","observation_id":"1f7285b4-540b-45e1-9120-01e1325dd4e5","resolution":{"observed_at":"2026-05-14T09:17:58.161400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Optimization , volume=","venue":null,"work_id":"3ce80302-8817-459b-9021-d20276a7e1af","year":2024},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:b40c4288848106e700d67b7b0b0e8e32dfc97fc70c946a3056cca10763a43249","observation_id":"19ec82c3-bbe3-4f48-a1b7-43db055f9787","resolution":{"observed_at":"2026-05-14T09:17:58.206950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Numerical Algorithms , volume=","venue":null,"work_id":"7a6b56ef-136f-495a-b69b-ca0abc2cef74","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:b6c54a764610a7c100ace02857cb83537e5e75fcb39ca47645a9ca6fddb16999","observation_id":"38018bc8-97d2-449b-a618-4ec3924a2692","resolution":{"observed_at":"2026-05-14T09:17:58.149460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"title =","venue":null,"work_id":"830ad508-d76b-42f4-8ec6-15d3d00d8935","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:ac265cf1d3c0fcba5c4d42b4e19b5db02d390c1f002a7140a9534d89d9081ea0","observation_id":"8dd57901-4233-4a1b-8bff-af489cdf27b3","resolution":{"observed_at":"2026-05-14T09:17:58.151055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A Unified Analysis of Descent Sequences in Weakly Convex Optimization, Including Convergence Rates for Bundle Methods , journal =","venue":null,"work_id":"32f37974-f021-4390-8b98-cd5e464a6097","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:cb22f715952d83388ed5c2d4c2d363f051a66655f12749c20219d0124b3cda95","observation_id":"bcebc1f2-6a51-45a9-ba48-6b33e5069e2d","resolution":{"observed_at":"2026-05-14T09:17:58.156223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"title =","venue":null,"work_id":"59edde3a-d5b0-4679-8a4c-0403725274b2","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:08dd9c3c038649e516579f32c5d389bfd99266ea351c7da7c05ca48d27c93bca","observation_id":"df8242c4-6d37-4a57-a4f8-601ec1b84841","resolution":{"observed_at":"2026-05-14T09:17:58.221653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"journal =","venue":null,"work_id":"a6aaa186-cac8-4c16-b816-911338c12736","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:b8726c4e6b1bcbe603e0c4d552b6972a8788bd24efee45c9b0defdc19fda8e0c","observation_id":"acf3dc5d-0a5a-4803-9f3e-7e22f0fa1d64","resolution":{"observed_at":"2026-05-14T09:17:58.167901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the","venue":null,"work_id":"7c75490a-aad3-472b-bae6-a80b3a6c6d62","year":2010},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:83b2201591234b8dad37f7aee229b687e58021648cad86eccaf5935c9dfacd52","observation_id":"19c347d2-7ab3-4f4a-a236-f259f82b7ceb","resolution":{"observed_at":"2026-05-14T09:17:58.201920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Bolte, J","venue":null,"work_id":"a741244d-544b-4e71-845a-e5d71adf741d","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:4e72ab61ff8aed67415df41aab2b8c5abeae4f139a7f66e19f15f0249d7e8fca","observation_id":"c85f0683-7c8c-457b-afad-fb78e76a133d","resolution":{"observed_at":"2026-05-14T09:17:58.270290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2024 , publisher=","venue":null,"work_id":"5a35c6cf-f3a1-43fc-90bb-89c802300c18","year":2024},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:c3afd32723f891605bdbc2ac4c20cfd01d6cd7e578b62ee3f299ba60b8824ec1","observation_id":"62670ed0-6122-4f5a-b6b9-40e86e7187b0","resolution":{"observed_at":"2026-05-14T09:17:58.213729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Control and Optimization , volume =","venue":null,"work_id":"1ef8e1da-406b-409b-8374-72ee71267ecc","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:0c4ed704fb771406677ce6f9ae9b8903d9e726e392fb82b0041676e0ecc52007","observation_id":"deb2453d-5e2f-4a29-bac2-70089cf7c8df","resolution":{"observed_at":"2026-05-14T09:17:58.203599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , volume=","venue":null,"work_id":"4f5284ed-b3fa-4b24-9ab8-35611a63ea41","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:6869df030b96e29c48c2d1e2f64ac1c4621ebf58966a54bda72744ea6e9ef5c5","observation_id":"6620040c-a2ed-4ad0-9f76-ecf3c4f2507d","resolution":{"observed_at":"2026-05-14T09:17:58.144523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , volume=","venue":null,"work_id":"7aeb8939-8553-4111-98b2-84e86640f17e","year":2023},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:9994624a3cf11e6a7c0fb48f32055ab6ed58594b02725ac2e96aa7fe5f63408c","observation_id":"874acf7b-d5a4-41bf-996f-585aeab74a89","resolution":{"observed_at":"2026-05-14T09:17:58.260256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"bca3ce53-31bb-43ba-8b68-693b4498a71e","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:7e1a53bbef67d32b5807775c900f7d20dae6b619d66699eb82e307e14db08385","observation_id":"d02151f9-d075-4f19-a833-7f6fe90f0be2","resolution":{"observed_at":"2026-05-14T09:17:58.248011Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Combettes, P.-L","venue":null,"work_id":"bcf88896-0646-414f-885d-762fd96738de","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:4fd104fefbadf80d9d10862ccccef7b40e53d92fbe810dfe1503d9da80da96a1","observation_id":"85e4b2fe-3667-4b4b-ad25-ef436106c76f","resolution":{"observed_at":"2026-05-14T09:17:58.254780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Set-Valued and Variational Analysis , volume=","venue":null,"work_id":"c3ce2e19-0265-4f34-b0a3-00e9f7228013","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:d3e4f5ceef097990b02ba816460f2da49e99cf6b7a8e83d96f89eff24e5807e1","observation_id":"37423667-ed82-4240-9b4a-3872289dd52b","resolution":{"observed_at":"2026-05-14T09:17:58.258547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , volume=","venue":null,"work_id":"e3711ddb-9594-47f6-a4d8-616ff975ccbf","year":2021},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:1540c449cfdeb2eef6250ae668cfcf1db337eff830f888ad1fa168d753abac4f","observation_id":"349c8311-8c0b-4842-b823-b52dae66ad6f","resolution":{"observed_at":"2026-05-14T09:17:58.215265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Imaging Sciences , volume=","venue":null,"work_id":"7e857858-c7c3-4020-b9a4-3c21c1ae29db","year":2009},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:ebe0d474f1ee550db623cd24271ac06b72d5df2b59ac8ef46bb78d8e9eacee06","observation_id":"0e2d537b-eab9-48ce-8e60-602a5d113eee","resolution":{"observed_at":"2026-05-14T09:17:58.209130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Optimization , volume =","venue":null,"work_id":"0f23d62c-44c2-4afe-9f0f-0e3d8f292a55","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:a4ef92bbb4c0158c198fee7f8f272f0611fa59a876f801984c5b780649822c29","observation_id":"feb15fb6-80af-417d-a376-46eb1a8767c5","resolution":{"observed_at":"2026-05-14T09:17:58.127850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2017 , publisher=","venue":null,"work_id":"3f4e43db-cfee-4246-85d8-96367c4c6008","year":2017},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:a063631b9ca2fb4dc598f5831ffa4b6dd228806cd2b8d8a9874497d58382dde4","observation_id":"58f462ab-4531-45d5-919b-6c09a62fb1c8","resolution":{"observed_at":"2026-05-14T09:17:58.261797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimization: Proceedings of the Fifth French-German Conference held in Castel-Novel (Varetz), France, Oct","venue":null,"work_id":"d283f9e7-8e98-4aad-88b7-e7bb35914ae0","year":1988},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:ab7095bf76907654cb4ea49ee9d2da460ad7cb253a11c6bc49a9d583c2a68629","observation_id":"3b316be0-bd64-49c0-88b5-a27286f7164c","resolution":{"observed_at":"2026-05-14T09:17:58.223276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Foundations of Computational Mathematics , volume=","venue":null,"work_id":"30ed70cc-c7a1-4d63-bd28-169810e23f1a","year":2022},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:85f04b580dcf1f5767e651bfe751d938eb9a08624cabb15dcd8d9d3ad4f6aaec","observation_id":"2da3b2ab-f962-444b-8feb-d0da0dd69889","resolution":{"observed_at":"2026-05-14T09:17:58.159897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Nondifferentiable Optimization , pages=","venue":null,"work_id":"c3432084-cbfe-48f7-a0cf-2cea1b1cd504","year":2009},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:1e6d8bff95b7403d451e4cb1b1814123054c8da82eb8e567b26ad0cc7e92e59f","observation_id":"3fa4df67-8c5e-4244-9e5c-a3d93566f4ea","resolution":{"observed_at":"2026-05-14T09:17:58.182593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.22332","last_updated":"2025-06-27T15:40:03Z","snapshot_observed_at":"2026-07-06T21:48:45.170162Z","submitted_at":"2025-06-27T15:40:03Z","title":"Second-order methods for provably escaping strict saddle points in composite nonconvex and nonsmooth optimization","version":1},"cited_work":{"arxiv_id":"2506.22332","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.22332","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Bodard, M","venue":null,"work_id":"1864610a-0642-431f-a9db-3b2c111f107e","year":2025},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"cited_paper":"/paper/2506.22332","citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:890135b0fc218441b768bca6b39c951c3c30e9038fd6911ed720458aa8a3ca01","observation_id":"4c4143a2-53e9-4e45-975d-a562fb7b7201","resolution":{"observed_at":"2026-05-12T08:36:25.862037Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"title =","venue":null,"work_id":"8fb02ad5-3a49-43a2-990e-c4b076cf4458","year":2007},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:8118c6cf7c0430677ace8f1ff1d4410f6b56fd5fdd38643e2f6fadbcfa5c4440","observation_id":"94b84e15-ab56-4155-8842-5cbbdf977d96","resolution":{"observed_at":"2026-05-14T09:17:58.185970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Optimization , volume=","venue":null,"work_id":"908054f3-8e92-4898-906b-6e6ff4b58368","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:003dd5ef6cc6e346d27c14bc45e8f06aa879a576c767fa07671c48d1695c9bfb","observation_id":"ea3bd607-dd2e-42ed-9edd-f3af15a2e72c","resolution":{"observed_at":"2026-05-14T09:17:58.228039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proximal alternating linearized minimization for nonconvex and nonsmooth problems , journal =","venue":null,"work_id":"7690139e-5e43-43e4-b558-264e78b4d707","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:8e2fd860a36ccb1d5a2702bb843afdfccbd7543205687f2b4a42465461646169","observation_id":"24adad36-7308-474b-9d13-b2b57d6a3536","resolution":{"observed_at":"2026-05-14T09:17:58.198728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Optimization , volume=","venue":null,"work_id":"3a9abc84-b70f-40f7-8c56-4b03039e126e","year":2020},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:01228c1ca753eb1fbca45399fc3dcb7379bdb1e25b8dcbc506e1d09c6819a44f","observation_id":"9419bde0-b2d7-40a6-b656-bd7cbf497f29","resolution":{"observed_at":"2026-05-14T09:17:58.229905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"TOP , volume =","venue":null,"work_id":"beda9b07-1a99-4870-a514-6a1bd86a896e","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:648444344bac16a12287ca238ed8770ecf4f72262d5cc26aebde4d9179cf71a1","observation_id":"eced144b-010a-4baf-9e58-56c63524e614","resolution":{"observed_at":"2026-05-14T09:17:58.130076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Scientific Computing , year =","venue":null,"work_id":"551adf88-4677-4bbd-bf89-8f2d80ad492b","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:4ce9f4e1c98ed402ebc3638ea16ba257eca8225dcd869632c9591c30b668a2e6","observation_id":"b604edc1-43a0-4213-bf15-83b3cac73d8f","resolution":{"observed_at":"2026-05-14T09:17:58.212285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T06:36:52.731120Z","title":"2004 , publisher=","venue":null,"work_id":"5fa9cf49-1090-4df1-9792-4dad5cc16fa0","year":2004},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:683ce79171f8bb8987ec1d165740eab4620dc9772a7260097f120dd36b5921ad","observation_id":"ab12814c-4955-4b5e-951e-ddb28e20e3cb","resolution":{"observed_at":"2026-05-14T09:17:58.114992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Inverse Problems , volume =","venue":null,"work_id":"27827df5-11e5-49d9-99b2-f9257206fedb","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:6be7006a50e029fb8b047ba8fa161cec38deffee765f17c46b54e9f39009a1ab","observation_id":"5803147c-2b70-4c58-99f8-819a0a84067c","resolution":{"observed_at":"2026-05-14T09:17:58.239782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Naldi, Emanuele , title =","venue":null,"work_id":"86de0a11-c1b3-4756-acdd-2caaa57d4948","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:d16b09ac4316e13ecf217d64eb862a0c99b02f315f0c9b56d48dbdac80ac8670","observation_id":"05a977d3-c46b-42d5-990f-89ffdb339f48","resolution":{"observed_at":"2026-05-14T09:17:58.111243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.04375","last_updated":"2026-04-14T19:17:46Z","snapshot_observed_at":"2026-07-06T22:24:04.107920Z","submitted_at":"2025-09-04T16:32:43Z","title":"Extending Linear Convergence of the Proximal Point Algorithm: The Quasar-Convex Case","version":2},"cited_work":{"arxiv_id":"2509.04375","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.04375","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Extending Linear Convergence of the Proximal Point Algorithm: The Quasar-Convex Case","venue":"math.OC","work_id":"274d9689-8e42-4c4b-a95e-80edfe09b3ce","year":2025},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"cited_paper":"/paper/2509.04375","citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:6154a61b981a18ba14b3b2827d3a112e9cd4833266d03ea26f8ed57d856aa3c0","observation_id":"259e362c-9841-4774-a859-4974422a2891","resolution":{"observed_at":"2026-05-12T08:36:25.853702Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"34fffb7a-17e6-45f6-87a4-6c9ada0d1255","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:d2f663932798971c4ca4544ac7506da5fb1186a6e38e548b82c3b532f87be6cd","observation_id":"fb6255bf-7ef4-4114-9853-b2eac7764350","resolution":{"observed_at":"2026-05-14T09:17:58.116937Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Optimization Theory and Applications , volume=","venue":null,"work_id":"327e3162-3b89-495e-8a3b-b49e0136bfeb","year":2014},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:34d0f315e400ee25c169b1b4e8d69a131201ef2aba994a252de1d047e015f3ea","observation_id":"362cd924-7123-4520-8990-f76cd5c73110","resolution":{"observed_at":"2026-05-14T09:17:58.241574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Hoheisel, Tobias , title =","venue":null,"work_id":"e80a345c-844c-4c7f-a1a3-fca4064ee1d1","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:4866be3f713c669f5352d9a1b6df7106532b47d8ceac51671d33d8fd38c3dc63","observation_id":"ef793e53-a3d5-42e8-a8d3-12f7b97b97fe","resolution":{"observed_at":"2026-05-14T09:17:58.306533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , volume=","venue":null,"work_id":"d2e71098-b218-4213-b351-93836ef0399e","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:068e5ca5499bb9895bf6004c4d463915bc4d8826e785d49091ba4033b5d7bb5e","observation_id":"839990c7-b8de-4101-b9aa-94795939c112","resolution":{"observed_at":"2026-05-14T09:17:58.298537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International Journal of Mathematical Sciences , volume=","venue":null,"work_id":"08f06eee-2fb4-408b-8eee-a7ffbff183a5","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:ccf7bdb4667c640cce14f3096dfe63d8bbb59212fa9d8f2a0049e005eb65d99b","observation_id":"df49d605-1a4c-4c04-bf61-a883266d1f22","resolution":{"observed_at":"2026-05-14T09:17:58.301491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2009 , publisher=","venue":null,"work_id":"2e83ee5e-2ca8-42ad-933e-cf94c3ee770b","year":2009},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:5e06f055c6c6f73f46eb5e23dd51b08ec154064e8657d930368c26dd3c90a262","observation_id":"c4f1eea7-3db9-4fa9-a408-c9452723152b","resolution":{"observed_at":"2026-05-14T09:17:58.332789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"title =","venue":null,"work_id":"9775398f-8d3d-4734-b972-9e5513534d17","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:9a46120f167d338be866f48bd9773a82eb129bd42ed8774db34423724af7db34","observation_id":"877b7ae5-0653-4c64-b53f-84f5a66754d2","resolution":{"observed_at":"2026-05-14T09:17:58.246470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Zhu, W","venue":null,"work_id":"88dd43c1-61f2-457c-bbcf-a9d6acddabea","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:460c031680e81b5ab1f45fbdd1e37a911b507674c0b8803f303d2fc507933022","observation_id":"1d31c402-75f9-4a9a-a7c0-ebb4f103a93c","resolution":{"observed_at":"2026-05-14T09:17:58.244841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Zhu, W","venue":null,"work_id":"755fa931-72a5-454b-bd64-591cbc337fd3","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:29dcfe39a95085f1dfd5e09cf1da555c170bf532f6bdc4bee13f8127d711e924","observation_id":"c869957d-865f-4773-b0ab-7162fad2fefa","resolution":{"observed_at":"2026-05-14T09:17:58.107858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14633","last_updated":"2021-10-27T17:59:46Z","snapshot_observed_at":"2026-07-06T12:02:46.237292Z","submitted_at":"2021-10-27T17:59:46Z","title":"Similarity and Matching of Neural Network Representations","version":1},"cited_work":{"arxiv_id":"2110.14633","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.14633","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Similarity and matching of neural network representations , year =","venue":null,"work_id":"4d5e7dc5-2187-4975-b670-39b877def34b","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"cited_paper":"/paper/2110.14633","citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:bff6343da5927eaba57951a0462f83f7b54c62e6a37532916c9beac8d460163f","observation_id":"aaeb807b-8d79-42d3-bb4c-41b5841eef5d","resolution":{"observed_at":"2026-05-12T08:36:25.833909Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-08T21:25:38.941518Z","title":"title =","venue":null,"work_id":"2880c6f3-0b8f-484d-a13b-7ec450ab51e6","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:780cd72a2601109e9005d5a43e0a3248d6be1300336b6a21a4c871e78f84da0a","observation_id":"479420f0-134a-4dad-9945-a0adcf001269","resolution":{"observed_at":"2026-05-14T09:17:58.295242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2013 , publisher=","venue":null,"work_id":"c0aef8d8-e827-4c2a-abea-70c91d300679","year":2013},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:4b26e1fc393d73c62bdc1d2baac137885b629d15170d5c64d33bcac0f4d4b83a","observation_id":"a9d172ec-7291-4e9f-ba8d-4faf264d2b3c","resolution":{"observed_at":"2026-05-14T09:17:58.320800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4f924585-7ace-4e31-9339-e426635a4068","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:b94bc44f698c07218b54829c77e40d235cddfb9a26341752a558fe74a50dbb4d","observation_id":"436fd450-1200-4a6b-9ca5-be202618760b","resolution":{"observed_at":"2026-05-14T09:17:58.408152Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Continuity and differentiability of quasiconvex functions","venue":null,"work_id":"dc036c35-faab-4c39-8cbb-b0a513416c9a","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:d3dcd7687f6d6eae5532c026465bf0a760834c62ef515f845a1400f08e996afe","observation_id":"8f030045-22a8-45b0-bfc2-fcf451bb9c73","resolution":{"observed_at":"2026-05-14T09:17:58.249651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Paquette,Courtney , title =","venue":null,"work_id":"38fe7afc-6908-4694-a429-9ebe701f0074","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:df830527ae51f3cd892573c41cb43ccc7a22bf39514b7fa53dfdae8d0a1bc6bf","observation_id":"3d31d4fe-4e1b-4326-b959-40fb15a6fe83","resolution":{"observed_at":"2026-05-14T09:17:58.252968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Foundations of Computational Mathematics , volume=","venue":null,"work_id":"340265b7-bcfe-44a1-b952-498f4ea10adc","year":2022},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:a51cee76c7ec625775e113a392639e72f73dc456663a7f779f9df7cceed590d8","observation_id":"2a137eaf-6b76-4fa7-a5f1-44f63b51ae4c","resolution":{"observed_at":"2026-05-14T09:17:58.098646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Escaping Strict Saddle Points of the","venue":null,"work_id":"de77b9cf-baab-401b-a6bf-1dd16dc6a5fc","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:e16ef13f21ad8507a8b75d114e0fd2726aec3613218ef747df72c1dcf620f429","observation_id":"7f85b1a5-351a-425e-8f8e-31097ed0504d","resolution":{"observed_at":"2026-05-14T09:17:58.102541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2012 , publisher=","venue":null,"work_id":"205b2d70-6952-424b-b7e7-365fcdbe54a9","year":2012},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:edef4a3bfa36b51b41fe631925d605217e50f9cea0378b02548bdfa2963f045d","observation_id":"60b0abfe-4875-43c7-9f21-a843b68f7db7","resolution":{"observed_at":"2026-05-14T09:17:58.100771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.06038","last_updated":"2017-12-17T00:54:54Z","snapshot_observed_at":"2026-07-06T06:14:42.724196Z","submitted_at":"2017-12-17T00:54:54Z","title":"The proximal point method revisited","version":1},"cited_work":{"arxiv_id":"1712.06038","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.06038","snapshot_observed_at":"2026-07-04T06:59:37.614953Z","title":"The proximal point method revisited","venue":"math.OC","work_id":"aea69cda-e1b9-4d8a-a622-b474a3999399","year":2017},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"cited_paper":"/paper/1712.06038","citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:c18dbc51783f9e4122b91386ceac6bec30acdfb1ccffc1f5a16aa67e22f2b2a1","observation_id":"63c6b933-6a5f-49fb-9f99-71d320623078","resolution":{"observed_at":"2026-05-12T08:36:25.828004Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , volume =","venue":null,"work_id":"a1acd3d4-bcff-4b67-8406-3d40571dd993","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:49e987eaf142a1a653956e5c4f59c1a6e55f1e5b47a249f0748f284f86f576c4","observation_id":"8a1bb3cc-6b7d-466a-ae3d-cf15472bff1a","resolution":{"observed_at":"2026-05-14T09:17:58.118554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Namkoong, H","venue":null,"work_id":"d448c50b-5735-48c4-be0a-af4c37051d07","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:9b75d7f138a381caa272d305a4d3e49a7220a1675742df12149e1b0fa5da6f14","observation_id":"a5cd711c-418c-4167-9de6-29ba6c2cdde1","resolution":{"observed_at":"2026-05-14T09:17:58.142610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"40b075b4-4d1d-476f-921d-3b7b031a8b7f","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:9240fd9a85040bf114fb624fca04c4d69aba55b60d170612f5d5bc7e21573a17","observation_id":"445211be-af9f-49ea-b94b-491896fb4983","resolution":{"observed_at":"2026-05-14T09:17:58.177589Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.02281","last_updated":"2026-06-22T23:48:14Z","snapshot_observed_at":"2026-07-06T21:18:50.421957Z","submitted_at":"2025-05-04T22:43:57Z","title":"Minimisation of Quasar-Convex Functions Using Random Zeroth-Order Oracles","version":3},"cited_work":{"arxiv_id":"2505.02281","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.02281","snapshot_observed_at":"2026-06-24T02:14:20.635556Z","title":"arXiv preprint arXiv:2505.02281 , year=","venue":null,"work_id":"bbf0e8ce-b1f8-4b1f-a655-3a82367f34ad","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"cited_paper":"/paper/2505.02281","citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:f5b94ee928712c9ef8a9b1b067996feb46ad6ca7782866a88e1d60c7f8205e0d","observation_id":"42debc3d-5385-45c1-91b8-03e7cd2cdcc1","resolution":{"observed_at":"2026-06-24T02:14:20.635556Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Optimization , volume=","venue":null,"work_id":"c0628f46-ffe3-40e3-bf44-dadbc4b08e33","year":1996},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:a71d6f035cd9a91a8044343e9164d4da95ac355226a33136b43e5a9ad89e0d19","observation_id":"43cfd09b-6d9f-40c3-88a6-567f962a417e","resolution":{"observed_at":"2026-05-14T09:17:58.393503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"International Journal of Systems Science , volume=","venue":null,"work_id":"0376e0f0-3ab1-489e-bc5c-f6bd4006ae9d","year":1981},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:f16c6495eb00141ae8c919a7d5083f4084151233ba1ea429ecaaf0bad4376a94","observation_id":"d6954cc1-41bb-463e-b46b-2c3115850198","resolution":{"observed_at":"2026-05-14T09:17:58.403385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Applied Mathematics and Computation , volume =","venue":null,"work_id":"693b3bcb-9b7c-4cde-98e4-a8b7fef477b5","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:bb2c8b467b9c6c3c9208e6bbca311183aba43d9189042e91fb61cd93d6474fba","observation_id":"8afa34f4-934d-48e0-99f2-5dabaedbd64b","resolution":{"observed_at":"2026-05-14T09:17:58.372222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Information and Optimization Sciences , volume =","venue":null,"work_id":"a7b48572-8ab4-4258-ab6f-2b1cb57fbb89","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:57e4ccf02c10015fffb339ea405487be46b84710f9ab9d0f06f0658ba79d3895","observation_id":"77cacbf2-eb65-4598-bf6a-17b97398c491","resolution":{"observed_at":"2026-05-14T09:17:58.364276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Dini derivatives in optimization—Part","venue":null,"work_id":"58ba57ea-2e52-4a46-9592-d145f2bcc4ef","year":1992},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:4f1f1d6f0071fd8d9b71c9ac02de51b703c0d87cd78b68416979432af7556480","observation_id":"2c54e3b0-c187-47e9-a915-601f783fdf9c","resolution":{"observed_at":"2026-05-14T09:17:58.360892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Optimization Theory and Applications , volume=","venue":null,"work_id":"2ee011d9-e36f-42a1-ba3f-efcf54c8a8a7","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:a1fa0ecb4fec684246d93366fd5ee48d1ff80894bcdb91db9795dd386b4ce22c","observation_id":"072905a0-0365-4362-b5ce-f4429f0455c4","resolution":{"observed_at":"2026-05-14T09:17:58.365885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Multi-Stage Multi-Task Feature Learning , volume =","venue":null,"work_id":"3d651e3d-50bb-42ac-986e-5860d8f6d6f3","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:939d23c75013d179a56d2546de61fad19507e67cc691ac4c59aa4c293ccb10d7","observation_id":"b691041b-ee95-4333-afef-8a04ce04fe5e","resolution":{"observed_at":"2026-05-14T09:17:58.342511Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , pages=","venue":null,"work_id":"745c9b3c-3e19-41f1-83d4-19d8d930864d","year":2025},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:1ddd21f3f9546bfc13a0d579e83fc03f8139450a369b29174247c945fe9e03c1","observation_id":"c1bca06c-ca8b-4bdd-9112-489b1aec1d79","resolution":{"observed_at":"2026-05-14T09:17:58.340837Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Optimization , volume =","venue":null,"work_id":"26a58079-6fab-48b0-b82d-164f9376dbc3","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:049773551f37fc3a0af4dd6d7a1a1c3ca770bace4f5ee9f09a83caa2c6481783","observation_id":"af3c4354-e8e5-4e82-a4ef-2ebdce17af38","resolution":{"observed_at":"2026-05-14T09:17:58.354705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"On the Convergence of the Proximal Point Algorithm for Convex Minimization , journal =","venue":null,"work_id":"c4b95c4c-7716-4827-889d-e749e8910217","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:0216aaec8e24dd49d00546e5db5ee994d2d6220c2c3f2854b766228babcd7d41","observation_id":"0889d324-9d69-42e8-b508-fe01b3f0529c","resolution":{"observed_at":"2026-05-14T09:17:58.373871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"New Proximal Point Algorithms for Convex Minimization , journal =","venue":null,"work_id":"7d723070-7ea7-491f-a8be-c2122cf7acfc","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:908bcc4c99fe8b583511057cb84758843bf76f5f326606227d6fef94a2e0da17","observation_id":"b7b13a4a-5dba-4379-9310-581161041613","resolution":{"observed_at":"2026-05-14T09:17:58.327674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Guminov et al","venue":null,"work_id":"44443d44-ae13-4852-9ec6-a49b27fda4f8","year":2023},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:2c08e32dbb25919b1c49158dbebe569f6e69682d44f23543b30085de568b6564","observation_id":"f511d77d-d485-49c5-8477-a131d086ae3d","resolution":{"observed_at":"2026-05-14T09:17:58.339173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2405.19809","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T11:23:20.578354Z","title":"Study of the behaviour of","venue":null,"work_id":"788be521-bd6a-4e9c-9c43-7a41d5d76599","year":2024},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:c7434736d757414dc454cf469b1ec90ed8c6420b3cffd277dd338d828dc50ffe","observation_id":"57bf5910-2239-4830-beae-528da8f45693","resolution":{"observed_at":"2026-05-12T08:36:25.848813Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2005 , publisher=","venue":null,"work_id":"655ee6f3-1331-4d2f-b176-092faa3552a1","year":2005},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:e63078e242aac64ce40cf000da39d52c761b904f97bb1e91757dc21c6a988610","observation_id":"06c54476-951f-48dc-a998-81ae957511f7","resolution":{"observed_at":"2026-05-14T09:17:58.309526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Lara, F","venue":null,"work_id":"1873536d-330b-476a-881b-8666220860bc","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:12baeb514a1e5b969ac6aafb9b07e8cdceadca19418df3280a652fc0e6d6baba","observation_id":"1d71159d-07f5-4ac7-8cdb-ef53e17b7454","resolution":{"observed_at":"2026-05-14T09:17:58.190803Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Machine Learning Research , year =","venue":null,"work_id":"c18ce418-e41d-4870-8ada-87bbe8b02568","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:b8b204cd7bebc8438fb21a0ba45e0786e5b81f6fd12b13536c623825efcf5611","observation_id":"1e0e9f6d-baef-45ef-82f2-93baea29f8d3","resolution":{"observed_at":"2026-05-14T09:17:58.398291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Proceedings of Thirty Third Conference on Learning Theory , pages =","venue":null,"work_id":"78be33de-f977-4410-b75d-487571a6ce18","year":2020},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:43195920d92a51252693bd67d1bc86e1983494f759bb82e349d4626dca530117","observation_id":"1d6b058c-7793-46dd-8da8-d2ed148f9236","resolution":{"observed_at":"2026-05-14T09:17:58.405106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"1996 , publisher=","venue":null,"work_id":"29ae4c29-978c-47c2-ab1e-fd3b4c1501c8","year":1996},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:381e5dc5239bebaf0b66e8ad845cb940ce88522c79026414b658c290784efaaa","observation_id":"f1db0c7b-b60a-40db-8110-d400df5d13f7","resolution":{"observed_at":"2026-05-14T09:17:58.388204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a5a9095c-1a4f-486f-9ae0-c34861f14471","year":2021},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:9c65c323732d125e8f05843bf426c364d2e049c15c608bae0d984321d23463f6","observation_id":"6c9dd47b-d7e6-40b1-a937-45b25eda2c2e","resolution":{"observed_at":"2026-05-14T09:17:58.216762Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Shi, L","venue":null,"work_id":"6add43e4-8840-4b29-94a2-ea07d7a9eaa5","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:2fa03c72a2c592e18b909a1c8fedf2e3e99e81da5db75aa656a3b043b00b1f2b","observation_id":"b6cad0c7-5a05-4fde-9547-ae12c976b149","resolution":{"observed_at":"2026-05-14T09:17:58.135323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Programming , volume=","venue":null,"work_id":"74def958-ae10-4eb9-bb41-1cb7ec9719f3","year":2022},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:86a2cf2ed0eaf7f277eef867becb3b01a11672519561420716b52a9c1af0cd44","observation_id":"e74c0e01-4bc6-46b0-aea4-67ae308c8da0","resolution":{"observed_at":"2026-05-14T09:17:58.152670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"49406946-49ed-4c93-b927-998be710a3d2","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:3691a560ef7caf6cd15eb6a2dbb0c58f67f797e55becf08f7d5389046f779505","observation_id":"93ed1af6-65f1-442f-83e0-ebb4a8e7f3ea","resolution":{"observed_at":"2026-05-14T09:17:58.158056Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Optimization and Engineering , volume=","venue":null,"work_id":"c495e28a-0cb8-4404-bdd0-82cbc1953099","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:64d540c6fbd7fe050c058052e14f36552e9edbc3e1be3bb2c2d959cfb0b2fd09","observation_id":"511db8ad-4873-4ba2-9d6d-e07d388b6900","resolution":{"observed_at":"2026-05-14T09:17:58.218440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Convex Analysis , volume=","venue":null,"work_id":"1c8ddd2b-27ec-430f-b082-2f01c553c72d","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:3f4f8bcf9478c370978281b9b794c81300f9b9bfcacffe32678f7e23165d1039","observation_id":"6a446a7d-c715-464b-b121-b2b1b09446d5","resolution":{"observed_at":"2026-05-14T09:17:58.251427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Optimization Theory and Applications , volume=","venue":null,"work_id":"a88f28a1-829d-41ba-b2ae-d3e8ffe90a60","year":2022},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:83bd62e19661b84dfc762f3f1e66d39204bbf769bc643c2800f2ece897304b41","observation_id":"c0e34124-c118-4376-96e2-b2a674c54ad8","resolution":{"observed_at":"2026-05-14T09:17:58.256510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Lara, F","venue":null,"work_id":"b88df2ac-d293-4236-bea5-9a1b86116ac8","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:9b89ce181d28eecc55451b950a55481eb4ee2d33dd7a6c31d385bc9fd8e28450","observation_id":"cb5a0d66-9645-45ad-8ab0-49741dc41210","resolution":{"observed_at":"2026-05-14T09:17:58.220148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Certifying the Absence of Spurious Local Minima at Infinity , journal =","venue":null,"work_id":"c45cef16-b5fc-43b3-be11-86c6f3fb6195","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:eed30837958d75853940a8124c56e5a888faf58325782693868e90e1dfeb0ca8","observation_id":"9770f530-52a3-4ba4-837b-f88429b34e19","resolution":{"observed_at":"2026-05-14T09:17:58.133597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Control and Cybernetics , volume=","venue":null,"work_id":"b1bbd29d-7a80-40ae-9736-d3ef44764bcf","year":1996},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:e132ee82d1cd9d37615ed048b03899a5399eaf8e67059d8fa36acf2a113e3a76","observation_id":"7279dc0f-8e24-454d-b0d5-bcbe3fb8537a","resolution":{"observed_at":"2026-05-14T09:17:58.136995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mathematical Notes , volume=","venue":null,"work_id":"09910f25-d371-4851-b601-8ae9dc49eb23","year":1996},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:8b4dc7ddccc7aca9ca59dad36d61980a8ce2b55b66f4f37f7c4b7a94bb010416","observation_id":"d281ba43-f6a5-42e5-a801-87676732c46f","resolution":{"observed_at":"2026-05-14T09:17:58.210714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Ahookhosh, M","venue":null,"work_id":"436c06c7-d4ac-4fb2-a70e-8d121dc643ea","year":2024},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:c351d4785ccec22856d1e620c619664b5aec3adb769fdfcc5b324046cc7dd793","observation_id":"13d06d26-97aa-4706-9b83-2f30d81b3646","resolution":{"observed_at":"2026-05-14T09:17:58.154322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Ahookhosh, M","venue":null,"work_id":"8c035218-dc69-4196-b1d4-e9b35ff45b83","year":2025},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:9b3364f1c6c6f23de83f5d83aa7b696ae3048bafd86f33579c03aa22a1682885","observation_id":"da14e393-ca36-4a89-b738-026ada2dc3ea","resolution":{"observed_at":"2026-05-14T09:17:58.224818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Global Optimization , volume=","venue":null,"work_id":"9d77d15e-4abc-4868-ac87-69db76c3d08d","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:371e4dbb3601e3fb2f9f0dc2d6792461cc3d30a6ccd6d4c2bd86942fc769ee55","observation_id":"8285274a-9404-4988-8421-1eca43b9c61f","resolution":{"observed_at":"2026-05-14T09:17:58.187512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Ahookhosh, M","venue":null,"work_id":"5a3047aa-46de-486c-888e-e6c6c8087cd9","year":2025},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:fc3d60edc8bc6aa0a8ffbd218273c4a744b332bc7971ddc20d2f746ff34710d6","observation_id":"d40ff3ed-b1d4-444e-8a5d-11b00579334a","resolution":{"observed_at":"2026-05-14T09:17:58.163035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Journal of Global Optimization , volume=","venue":null,"work_id":"5bd925e4-a1c2-4eaa-b89b-f4e8e596b544","year":null},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:1fd79dde132364a28f10152e0e4bc8faf2fab5302e628c2472f31af9f6cd810e","observation_id":"6ae787c9-2b05-4a19-95c0-94c5bdd6bafb","resolution":{"observed_at":"2026-05-14T09:17:58.226381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"2015 , author =","venue":null,"work_id":"843cfdb8-694d-4cda-93c8-37963aebfa01","year":2015},"citing_paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-05-12T00:56:13.855741Z"},"links":{"citing_paper":"/paper/2605.08474"},"observation_digest":"sha256:e61b90caa5e5081fceba7bc44e0b9a03e25fb2c246ea3a4ec93e496ad4acd90a","observation_id":"221578b7-2d77-4b7a-83de-ff7106555ee9","resolution":{"observed_at":"2026-05-14T09:17:58.131906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.08474","last_updated":"2026-05-08T20:50:40Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:50:40Z","title":"Robust Learning Meets Quasar-Convex Optimization: Inexact High-Order Proximal-Point Methods"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":7,"verified_exact":5,"verified_fuzzy":86},"total_outbound_references":201},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 100 of 201 outbound references and 1 inbound Pith citation observation for arXiv:2605.08474."}