{"as_of":"2026-08-21T08:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e2819fc3da080cfe1ef443c2dd073bd0d5d260aff295e6842cacb6554b890f3e","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T18:44:45.489284Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2506.19883/citation-record","integrity":"/paper/2506.19883/integrity","json":"/paper/2506.19883/citation-record.json","paper":"/paper/2506.19883"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.961969Z","title":"Uncertainty-aware search framework for multi-objective bayesian optimization","venue":null,"work_id":"0200cbb0-9891-45c0-a798-171d05fbfa09","year":2020},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.084761Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:4303e90a9fade062d1d15efc4575b617c26255cc3fd2a21931c662e2fd3a538e","observation_id":"578d0cc5-8189-4b35-8338-c18e428f2d75","resolution":{"observed_at":"2026-08-15T18:44:46.982430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.091623Z","title":"Convex optimization","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.091623Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:58dee14fbcdfa3a470aa8341bb674b2de495dfd3b0a138840decfa2cf59fe0d5","observation_id":"16994075-b9f8-409c-9a68-3a954d422a32","resolution":{"observed_at":"2026-08-15T18:44:45.091623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.098046Z","title":"An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.098046Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:3443177524dfdf0f90e91649c620f1f67c553b8b5e29ff484d15eb5fac1b8fab","observation_id":"4b20ed7b-08c7-413d-9530-f643ee853b82","resolution":{"observed_at":"2026-08-15T18:44:45.098046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.877898Z","title":"Co-attentive multi-task learning for explainable recommendation","venue":null,"work_id":"42e2b873-f32d-411c-aede-7af044fc9aee","year":2019},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.106833Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:d80cfd7fad5ac969a27ff86c6b64acb100e0354d4edaffb0183d650585dc9eff","observation_id":"b5a7fecd-1d9a-4e90-be84-127036f27f1e","resolution":{"observed_at":"2026-08-15T18:44:46.890622Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.120054Z","title":"A fast and elitist multiobjective genetic algorithm: Nsga-ii","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.120054Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:3eeb6641574e57dea12ca078c8b422565c40183e2278999020cd2d189344e682","observation_id":"3ef0ebca-3ab9-4f77-8dc7-c04ca73acae0","resolution":{"observed_at":"2026-08-15T18:44:45.120054Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.131815Z","title":"Multiple-gradient descent algorithm (mgda) for multiobjective optimization","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.131815Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:8847d457e25de255f1782ad29b4e5438fbfc403c62c77971cbf1e90f2f7ce9b1","observation_id":"47e12c30-bb42-4b73-81fa-15a33c5dba2d","resolution":{"observed_at":"2026-08-15T18:44:45.131815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.782822Z","title":"Multicriteria optimization, volume 491","venue":null,"work_id":"92c8c897-9436-4919-bcfd-36d02df62aa2","year":2005},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.138943Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:46b82149f3adf1e2fbc1656e96520520ee720c4e4223b062166ac7c3185262e8","observation_id":"b01a1901-1b7c-4f96-aa7b-9b9e2e61236a","resolution":{"observed_at":"2026-08-15T18:44:46.791750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.746235Z","title":"Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator","venue":null,"work_id":"244cc86d-cad6-4a35-a490-be0e0302158f","year":2018},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.146745Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:b8007785eafc7a3b8e5ffac4b78befcbf7a1eb6d3f1e7fa335bfd613ccda8cb4","observation_id":"682f7f61-9197-468c-b419-b50b72e5da92","resolution":{"observed_at":"2026-08-15T18:44:46.760522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.12624","last_updated":"2024-03-19T15:47:43Z","snapshot_observed_at":"2026-08-17T16:12:12.847065Z","submitted_at":"2022-10-23T05:54:26Z","title":"Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.12624","snapshot_observed_at":"2026-08-15T18:44:45.160704Z","title":"Mitigating gradient bias in multi-objective learning: A provably convergent stochastic approach","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.160704Z"},"links":{"cited_paper":"/paper/2210.12624","citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:e2ea797a687c373eca8bcea8437c872a2f49d7d08c0225047c5e7ac576333149","observation_id":"d36e84d6-c568-4ce0-8392-7db6826a2023","resolution":{"observed_at":"2026-08-15T18:44:45.160704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.167117Z","title":"Variance reduction can improve trade-off in multi-objective learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.167117Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:4548868fcf7c38399e43c5071934f8295eb3fc85dc96c8150634c788ef7679a7","observation_id":"8c0079d2-5428-4aff-8d9b-c5c25426a303","resolution":{"observed_at":"2026-08-15T18:44:45.167117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.175784Z","title":"Steepest descent methods for multicriteria optimization","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.175784Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:9c31dd0c5a702fed09731ea6348a0f0f420987445233ca3a5d9711091b8779f6","observation_id":"3ac8218c-109f-4c88-8343-753f8f35615f","resolution":{"observed_at":"2026-08-15T18:44:45.175784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.682844Z","title":"Complexity of gradient descent for multiobjective optimization","venue":null,"work_id":"23d6b269-b6d4-423a-89be-78625a09689c","year":2019},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.186492Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:6b2675e9e63078abf218addf5444ec75d659ecd17e37948dc9917ada2ccc1ccf","observation_id":"3a43b0e1-cbbe-4188-803c-cac647a9d1fd","resolution":{"observed_at":"2026-08-15T18:44:46.696966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.197774Z","title":"Stochastic first-and zeroth-order methods for nonconvex stochastic programming","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.197774Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:f4b909c4365b90322325a2f414b47d51679dd2c61b60384663a7d68867ec4dc3","observation_id":"d54ed3ee-7a42-420d-bd33-e41e53e3fa86","resolution":{"observed_at":"2026-08-15T18:44:45.197774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-15T18:44:45.209373Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.209373Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:a174df0663ff58e143622da94b666a2b3f98560ff51d47512f928fad793551d2","observation_id":"3733dee5-db06-4c5e-88c5-a28d1ff7fbbc","resolution":{"observed_at":"2026-08-15T18:44:45.209373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.626580Z","title":"Fine-grained fashion representation learning by online deep clustering","venue":null,"work_id":"f0c6b07c-0d18-4b8c-bdf9-3e6e5924ccd8","year":2022},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.219167Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:67a3d7ecc2cb1a8ff4eae8ad4d3cec0cedab8b083d7a09452e0c26a0643b7b11","observation_id":"80ab2b85-5c32-4dba-83f8-e73ad3141bb7","resolution":{"observed_at":"2026-08-15T18:44:46.635752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.590952Z","title":"Learning attribute and class-specific representation duet for fine-grained fashion analysis","venue":null,"work_id":"3e3a04ea-8635-4d4d-b2c0-02324fe30c34","year":2023},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.233655Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:145b3bc974b2ae4340114a776746229462e3de4084e398f4c26805e887e0ecf0","observation_id":"8dd63bbb-cf0a-463f-a1b4-561314b07f66","resolution":{"observed_at":"2026-08-15T18:44:46.599980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.554645Z","title":"A deep survival analysis method based on ranking","venue":null,"work_id":"076a8dc3-6d65-493d-94cb-b58ab88e2281","year":2019},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.240389Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:6202dcb38106c94b7ed59d14fa72d60fd9444c99a509b28ae0489abc272832ac","observation_id":"dddf22cb-1943-4f46-89bd-280c4f9f23cb","resolution":{"observed_at":"2026-08-15T18:44:46.561049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.527413Z","title":"SCAFFOLD : Stochastic controlled averaging for federated learning","venue":null,"work_id":"300b5931-0a3e-4815-969c-7287b346e631","year":2020},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.247026Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:e405bc667a689c90ef53d5d6b2d3065986ea2e41c907023ccc8e4ba5e6463187","observation_id":"448859c5-9d98-4189-b496-f1cdea7a8aab","resolution":{"observed_at":"2026-08-15T18:44:46.533923Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.509461Z","title":"Deep attentive ranking networks for learning to order sentences","venue":null,"work_id":"c7ff5aa6-55ac-4e90-891b-6dcff51126ad","year":2020},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.252259Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:6eb3bb0e166a2174c6cd67582a378721db9d8c546d63952d8f809819e3a7a5fd","observation_id":"83af25b0-9b91-484d-8761-5393fc1f8bf3","resolution":{"observed_at":"2026-08-15T18:44:46.515027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.477500Z","title":"Bayesian optimization algorithms for multi-objective optimization","venue":null,"work_id":"80dfd810-0ae0-49c6-8489-54569350ad35","year":2002},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.261584Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:bbf5fdcd6acfe608e39dd763545dcb6300807d6e61138e5eb9c08b60998f0294","observation_id":"a6942fe9-18a3-43b9-9d19-e62c00838e25","resolution":{"observed_at":"2026-08-15T18:44:46.487473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.448262Z","title":"Mnist handwritten digit database","venue":null,"work_id":"5abb3561-2ff0-42db-a771-0beab45796f4","year":1998},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.268461Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:597dd6500c6559b0d61c049caf996df3e03d525a76e228d562f9c0843b22663f","observation_id":"b25ea2c1-ea68-45ae-a027-ffce48139073","resolution":{"observed_at":"2026-08-15T18:44:46.456380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.274157Z","title":"Pareto multi-task learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.274157Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:b1768d8e962302d7c95f0d4494ad875dd7a47a78a50e7bac5228fa6df20d7f7a","observation_id":"f9383946-7e6f-47e4-a75e-d776e1d667ce","resolution":{"observed_at":"2026-08-15T18:44:45.274157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.404672Z","title":"The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning","venue":null,"work_id":"61108194-644e-4a58-8526-d81d91eafa0c","year":2021},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.281177Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:8a3615c2444b65aa080640f5f3a994036b4341577d452631ffc20b309f75defd","observation_id":"d3063cda-6d25-41c0-b334-cdaafe29be90","resolution":{"observed_at":"2026-08-15T18:44:46.413336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.289854Z","title":"Deep learning face attributes in the wild","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.289854Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:ed9f56f69da152c523f14d7c16b1296491d3deed676de86351cfb87daa4d8e2d","observation_id":"164ab3a1-9b63-470c-bd31-498846fc9a07","resolution":{"observed_at":"2026-08-15T18:44:45.289854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.343845Z","title":"Deep match to rank model for personalized click-through rate prediction","venue":null,"work_id":"df3dd0ec-9259-45c9-9cad-87a836ffb71c","year":2020},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.296801Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:119e5b985b3ff0a0c8f0392d86ca0cfa600290956fd247c8fe3b4e87f4b7915b","observation_id":"51437907-27ac-4cf1-a79e-550df9b32956","resolution":{"observed_at":"2026-08-15T18:44:46.363786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.308380Z","title":"Multi-label learning to rank through multi-objective optimization","venue":null,"work_id":"0f854d02-c643-4573-8a92-18c32c70eaed","year":2023},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.307206Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:e5c671549a021a813eb384539d477ad80fae6882931cf006c7c91be321751dc5","observation_id":"c4a6d31d-bb16-4c6d-a6d3-a515fc829c1f","resolution":{"observed_at":"2026-08-15T18:44:46.317151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.271903Z","title":"Querywise fair learning to rank through multi-objective optimization","venue":null,"work_id":"7367c66d-0cb9-46b8-a85d-d46d3c8261b8","year":2023},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.314145Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:21f9cda1bde8406a4d199ddde9e733b6d4d547d7bc8388e7059e4f7f1c092abd","observation_id":"6efffcd9-cedc-4ec9-8b29-60355faa30e8","resolution":{"observed_at":"2026-08-15T18:44:46.280613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.326567Z","title":"A stochastic multiple gradient descent algorithm","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.326567Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:9a116d9fbf2aa2f7099778d2c7eb6e4acd3cd79d2c447b4a585de08ab6971e29","observation_id":"9961cba0-983b-4cb4-8703-e3b771344d51","resolution":{"observed_at":"2026-08-15T18:44:45.326567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.220666Z","title":"Nonlinear multiobjective optimization, volume 12","venue":null,"work_id":"65195a25-9e8f-40a1-9581-ac0a7171a469","year":2012},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.333896Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:edfd9c8038f8475b21673f96fe1e55f781e782cf44199ccc9189ba667f339224","observation_id":"7817ff58-caee-40e5-8676-7d9d79501078","resolution":{"observed_at":"2026-08-15T18:44:46.231336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.340398Z","title":"Algorithms for multicriterion optimization","venue":null,"work_id":null,"year":1980},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.340398Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:0397b68b83fb32a2361ba7f416fa22a9bbaba39b3c02e6909cb610a2c3d1c112","observation_id":"a6f86c30-70d9-490d-8e65-eaaff8887572","resolution":{"observed_at":"2026-08-15T18:44:45.340398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.165099Z","title":"Image retrieval with attribute-associated auxiliary references","venue":null,"work_id":"bbafc85c-264a-4df4-9ca2-a0d7a4e6f087","year":2017},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.347246Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:777a8201ec503d050a4e3c60fb1286912585e0a4acc1572e1e02b7be68f973d2","observation_id":"f0643648-1eea-48a0-8f2a-6a69e9217d90","resolution":{"observed_at":"2026-08-15T18:44:46.172745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.137744Z","title":"Policy gradient approaches for multi-objective sequential decision making","venue":null,"work_id":"9ff3befe-6e6d-4306-8fad-e3534d453a97","year":2014},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.359512Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:8d2f6a5f9a68a794f7268cc6dc16753986a32ac7de2088592dcf9e751e00db91","observation_id":"26bd4994-f090-4f8b-b0a7-5c730341c12c","resolution":{"observed_at":"2026-08-15T18:44:46.144492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.106196Z","title":"Multi-task video captioning with video and entailment generation","venue":null,"work_id":"47c04f61-db07-4fb8-8aa4-2d200b4d539e","year":2017},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.372588Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:7857280809ece5f28b3daca15728202542028a84d3c27dbd1b707db15010b316","observation_id":"042d57e6-1be9-4c98-90ff-8511be4c78f3","resolution":{"observed_at":"2026-08-15T18:44:46.114651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:46.071084Z","title":"Learning with average precision: Training image retrieval with a listwise loss","venue":null,"work_id":"2ec85f8f-ba6d-47c7-9567-b359feb196bf","year":2019},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.381442Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:f573e0204c9113e4cd4b84ce5ff56f0ee2d9a037317778d9a8c5fae940a46022","observation_id":"c2d2d529-e953-47c3-b172-891ca5e69943","resolution":{"observed_at":"2026-08-15T18:44:46.082131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.387863Z","title":"Dynamic routing between capsules","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.387863Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:0d00b6c854c25388fff5976eb6f0617acb13a642315dec5e547f08c9f803eb26","observation_id":"7615682f-e85c-47f7-9dc0-932f1d48060d","resolution":{"observed_at":"2026-08-15T18:44:45.387863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:45.395888Z","title":"Multi-task learning as multi-objective optimization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.395888Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:7934cdb8eeb5b4e7cd6e6987123e5cb06dd6d7d08403ef26e6cf5969016ffb1e","observation_id":"5fb6c279-550c-415c-886d-ced9b56d42c5","resolution":{"observed_at":"2026-08-15T18:44:45.395888Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02936","last_updated":"2022-01-21T03:28:27Z","snapshot_observed_at":"2026-08-16T17:36:47.918576Z","submitted_at":"2021-12-06T11:17:06Z","title":"Pairwise Learning for Neural Link Prediction","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.02936","snapshot_observed_at":"2026-08-15T18:44:45.405769Z","title":"Pairwise learning for neural link prediction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.405769Z"},"links":{"cited_paper":"/paper/2112.02936","citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:827ec9375805bbb46877e2059786e3d1620ea5fb7f1fc96e9bed7c03832c5b24","observation_id":"60cf1eaf-9672-4046-8d76-73f99a5061a2","resolution":{"observed_at":"2026-08-15T18:44:45.405769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T18:44:46.005523Z","title":"Deep multi-interest network for click-through rate prediction","venue":null,"work_id":"eee34717-bd24-4c15-9bbf-f4d3935bbc11","year":2020},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.413423Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:bb3bc77150b741d1629f8246e60c236e6314dd897a8fea011fe6793e25c916aa","observation_id":"e427d2b1-bf9e-4212-a405-992b913bcc66","resolution":{"observed_at":"2026-08-15T18:44:46.012401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:45.974305Z","title":"Empirically testing deep and shallow ranking models for click-through rate (ctr) prediction","venue":null,"work_id":"3ef57d56-7629-4d45-b80d-0ba162a493c7","year":2020},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.423729Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:a048fe8bff698b8e3bb6575140aa53f251bd94397307165575d1d797d36e3d6e","observation_id":"de381ba6-1d75-4f37-84d8-703bf2c3f25e","resolution":{"observed_at":"2026-08-15T18:44:45.982579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:45.933731Z","title":"Pareto policy pool for model-based offline reinforcement learning","venue":null,"work_id":"f9ba3bae-8255-4fad-8c9f-c678c7439392","year":2022},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.446641Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:86e1d7a48b644d4dcad015512a2a56c04ff5a2da962bf43077f4515430d7e876","observation_id":"b33a0fa4-ced7-46dd-b75d-f75418b24c57","resolution":{"observed_at":"2026-08-15T18:44:45.943217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:45.889302Z","title":"Wassrank: Listwise document ranking using optimal transport theory","venue":null,"work_id":"2305ea16-e679-4135-b705-05170db39ab8","year":2019},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.457457Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:02918201097ac67c2e07db151071d2c4b8874b4248658768a09a9583a3fb0a05","observation_id":"318221b0-de0d-43cb-bcfe-e9f2ed12098d","resolution":{"observed_at":"2026-08-15T18:44:45.901184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:45.858698Z","title":"Moea/d: A multiobjective evolutionary algorithm based on decomposition","venue":null,"work_id":"f6c8340f-85b4-447b-b613-b6480c278a80","year":2007},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.467998Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:323209412df9b394e35f01a64aebe3b3e991c3a09ccbb826061c924c15c90255","observation_id":"5a96f855-af54-46ac-9377-c6b100a630c8","resolution":{"observed_at":"2026-08-15T18:44:45.866888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:45.820754Z","title":"On the convergence of stochastic multi-objective gradient manipulation and beyond","venue":null,"work_id":"db21747e-d2c3-41c7-a3bd-3571c2988fd8","year":2022},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.479864Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:166073772d205aad003ad6e0e2071db2cf8b813fbd3cc3f889b121807b32d294","observation_id":"d9da26d5-cb45-4b5a-a74d-10ac1ff830b2","resolution":{"observed_at":"2026-08-15T18:44:45.833981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T18:44:45.788145Z","title":"Multi-task learning on heterogeneous graph neural network for substitute recommendation","venue":null,"work_id":"5d6feb6f-b2cb-4023-8e0d-97c591577694","year":2023},"citing_paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T18:44:45.489284Z"},"links":{"citing_paper":"/paper/2506.19883"},"observation_digest":"sha256:fbb2028f5fa289617c94120bba6d1b9e1beb3f1b892914ef693f6ee78399342c","observation_id":"45239a21-97b8-4894-8c5b-95985fab4883","resolution":{"observed_at":"2026-08-15T18:44:45.799362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.19883","last_updated":"2025-06-24T03:31:25Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T07:24:59.663116Z","submitted_at":"2025-06-24T03:31:25Z","title":"STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":28},"total_outbound_references":44},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2506.19883."}