{"as_of":"2026-08-16T12:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aa465e0388da4ea2096e265c1141dd85cfece5d791dc8c504ee2077fb4b44d5c","coverage":[{"denominator":85,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":85,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T20:33:48.356510Z","state":"measured"},{"denominator":85,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":85,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2412.05726/citation-record","integrity":"/paper/2412.05726/integrity","json":"/paper/2412.05726/citation-record.json","paper":"/paper/2412.05726"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:47.999304Z","title":"Bayesian inference for spatio-temporal spike-and-slab priors","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:47.999304Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:20def209d8c37d1d30252da5dceb5a75e5b6233056c9de2c88937502e4be07a8","observation_id":"111621fd-8b3c-466c-a103-0960f0f114bd","resolution":{"observed_at":"2026-08-11T20:33:47.999304Z","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":"2026-08-11T20:33:48.006054Z","title":"Gravity with gravitas: A solution to the border puzzle","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.006054Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:3f86760b332f65377af5721e99b11c3ef56fc4fa7e749de2598f4ce80c6028dc","observation_id":"65172441-deab-4415-ad9b-08ef8283b686","resolution":{"observed_at":"2026-08-11T20:33:48.006054Z","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":"2026-08-11T20:33:48.011685Z","title":"Structured sparsity through convex optimization","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.011685Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:791c9a923da65766afbef6a972866d07d217f4b4efe871031c0fc691b2ee7baf","observation_id":"b6efb229-3037-479e-8edc-6cdb6dbf5652","resolution":{"observed_at":"2026-08-11T20:33:48.011685Z","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":"2026-08-11T20:33:48.017929Z","title":"Optimization with sparsity-inducing penalties","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.017929Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:2dcdaf5d15243db0be7d28b1bd79ad877b56082ee0fb067cf6bcc1f468237a11","observation_id":"a3440eb8-f806-4a1b-972a-ea9ef3b65020","resolution":{"observed_at":"2026-08-11T20:33:48.017929Z","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":"2026-08-11T20:33:48.022475Z","title":"Adaptive regression and model selection in data mining problems","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.022475Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:97984944c3f88194ba13731e00be587b1b50c8a6a1d329c1c64fa8ba861a634e","observation_id":"043defe3-1fa8-4eb1-97cd-5bc833aa8701","resolution":{"observed_at":"2026-08-11T20:33:48.022475Z","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":"2026-08-11T20:33:49.579198Z","title":"Model-based compressive sensing","venue":null,"work_id":"a396f904-05b1-4ed4-9b3f-6f6746611181","year":1982},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.026431Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:7df2f64e2ef9a567817e1de99d07fffdd18302ed45e39f40ef859695cc01a5c3","observation_id":"c886d2ab-b7a9-4c07-88fd-0c727a8b307a","resolution":{"observed_at":"2026-08-11T20:33:49.582922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.567821Z","title":"Convex Analysis and Monotone Operator Theory in Hilbert Spaces","venue":null,"work_id":"91f00742-4196-49e8-89bf-62579617fe1d","year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.032200Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:afc2ba9bc098aa2dbf20bb283d850c419d56980304305ad958717f947afb7a88","observation_id":"10956b9f-0b40-4592-bc5f-d2da113a28cc","resolution":{"observed_at":"2026-08-11T20:33:49.572315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.557432Z","title":"Lasso meets horseshoe: A survey","venue":null,"work_id":"3a0b1a10-df8a-4e94-a0df-72ae6a4adfbf","year":2019},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.036502Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:e7b673ac6c2e13cbc65f205fc1cbc25ccd79daa2dc4c59dbad7714ea90dc9695","observation_id":"6147bc9f-32b7-49bb-8706-49d5e1334381","resolution":{"observed_at":"2026-08-11T20:33:49.561181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.6088","last_updated":"2012-12-25T22:02:47Z","snapshot_observed_at":"2026-08-15T00:34:31.339242Z","submitted_at":"2012-12-25T22:02:47Z","title":"Bayesian shrinkage","version":1},"cited_work":{"arxiv_id":"1212.6088","doi":null,"metadata_source":"pith","pith_arxiv_id":"1212.6088","snapshot_observed_at":"2026-08-11T20:33:48.822956Z","title":"Bayesian shrinkage","venue":"math.ST","work_id":"d640dfb8-2718-41ca-9515-b177e4a96600","year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.040401Z"},"links":{"cited_paper":"/paper/1212.6088","citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:daa7163f92642709fe4f3d81849122dac007f0ee04bdc30a41055848339f88c6","observation_id":"d2c58578-617f-4370-8d8e-72e11ea9fc41","resolution":{"observed_at":"2026-08-11T20:33:48.828143Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.045020Z","title":"JAX : composable transformations of P ython+ N um P y programs, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.045020Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:787ad5c8899161d4941647326d5864a86c6d4f150b6b8b473a4f4eb672cdcc7d","observation_id":"8f8fc589-4f4f-4d13-930b-b3abb27ddceb","resolution":{"observed_at":"2026-08-11T20:33:48.045020Z","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":"10.1214/07-aos0316a","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.458194Z","title":"Discussion: One-step sparse estimates in nonconcave penalized likelihood models","venue":null,"work_id":"ec2a5577-8160-4668-89cb-aaa1d287e83d","year":2008},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.051671Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ef63ee072b39ec5635f01ee58e933cdc1a71c1d77afe7343f88155d1bb78862a","observation_id":"1476a919-8da1-4060-8a78-f68bb8756ecf","resolution":{"observed_at":"2026-08-11T20:33:48.462360Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.536710Z","title":"Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information","venue":null,"work_id":"88588b4d-4d89-426f-8c58-ef4869fdc66f","year":2006},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.056280Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:a15889fb17ef83f108ff78b1573621c9d7a15a5e54fb8638eed3a31f8ca81124","observation_id":"288aadd7-ff46-433b-8159-22563c28d2b9","resolution":{"observed_at":"2026-08-11T20:33:49.541088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.525476Z","title":"Enhancing sparsity by reweighted _1 minimization","venue":null,"work_id":"010f4771-468f-4fae-bdec-762fe3e19fc2","year":2008},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.063528Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ffe7e6fca1fcdf0ced0778b2e9630b953a77059566266eb4d3c9f613b5d976b8","observation_id":"70dcb5ba-4282-4247-be77-464152ac58f4","resolution":{"observed_at":"2026-08-11T20:33:49.529352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.068140Z","title":"Carvalho, Nicholas G","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.068140Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:9906c47e01dbbec1591272c5bf53552063cf0bec3674f49689b81219b8da89a3","observation_id":"8b0616af-0174-4097-8963-cf842cc7a7af","resolution":{"observed_at":"2026-08-11T20:33:48.068140Z","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":"2026-08-11T20:33:49.514449Z","title":"Some statistical models for limited dependent variables with application to the demand for durable goods","venue":null,"work_id":"7e9f5af1-344c-4aa7-937c-c058423d0cdc","year":1971},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.072058Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:55571afa5efd5ea28d8a318b3fafce26e85d991de78d1dbcaa1e0fa95c0d4c67","observation_id":"46eae6cd-608b-4a28-adb3-de8163b46c89","resolution":{"observed_at":"2026-08-11T20:33:49.518145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10604","last_updated":"2017-11-28T23:05:15Z","snapshot_observed_at":"2026-08-14T20:09:07.109625Z","submitted_at":"2017-11-28T23:05:15Z","title":"TensorFlow Distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10604","snapshot_observed_at":"2026-08-11T20:33:48.076380Z","title":"Tensorflow distributions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.076380Z"},"links":{"cited_paper":"/paper/1711.10604","citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:0cc445c8d69fff102387f657e368c9a06c48d3b54375ba6bef84ce2fe43a5c6a","observation_id":"cfe843c6-27ca-4e88-a0e6-e951a3cd9091","resolution":{"observed_at":"2026-08-11T20:33:48.076380Z","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":"2026-08-11T20:33:49.502048Z","title":null,"venue":null,"work_id":"b7704d6c-f121-4431-8400-c3f234377ace","year":1995},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.080484Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:76ac1ba24e9c3eae8d256cac3d00b167037090982793403d2edef07d78a8f329","observation_id":"06bfc294-c199-4ebc-88cb-9845c0d15184","resolution":{"observed_at":"2026-08-11T20:33:49.505393Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.488020Z","title":"Boltzmann machine and mean-field approximation for structured sparse decompositions","venue":null,"work_id":"81fd8ef3-ff7c-4e64-8e23-4532d7054c44","year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.083719Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:33cbcfe73f386e13b8241509aeb6a41a4f4a317a23b39d1f522f80a1dcd49a23","observation_id":"4e085a9b-709f-43fc-8004-1f22a26ba752","resolution":{"observed_at":"2026-08-11T20:33:49.492159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.472519Z","title":"On the deep active-subspace method","venue":null,"work_id":"53b5023b-9b4b-4881-9dd9-154d22b28f7e","year":2023},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.086966Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:9612cecece30cc72f7a543f234a3ed595ebdff6fda2f8bca5e4efe568ee8583d","observation_id":"6ba2ab9f-b2e9-48e0-81b4-17acd7057596","resolution":{"observed_at":"2026-08-11T20:33:49.479127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.090939Z","title":"Least angle regression","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.090939Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:c488617937c9e6337606467d77e221a9b68334dfd7e2297d57a79d848dfb5396","observation_id":"868b3983-d13d-40b3-8c75-ba796f31ed12","resolution":{"observed_at":"2026-08-11T20:33:48.090939Z","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":"2026-08-11T20:33:48.094990Z","title":"Variable selection via nonconcave penalized likelihood and its oracle properties","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.094990Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:a4b73b0b48663f62fb9faa5f7f9bbda5f22d00ecb22628911c3e5293688284d2","observation_id":"2348e246-a5d5-4f5f-a00c-69de4b2e313c","resolution":{"observed_at":"2026-08-11T20:33:48.094990Z","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":"2026-08-11T20:33:49.452285Z","title":"Regularization paths for generalized linear models via coordinate descent","venue":null,"work_id":"a738b353-b699-4e9b-a1b7-7b0df8bfe97a","year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.098844Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:6cff104a4a30f19212b501daeae6ae76fdfd5afc8cde91e3e79d190cddf46352","observation_id":"f8ab21b7-2498-4fd1-bd97-5cb489b99aeb","resolution":{"observed_at":"2026-08-11T20:33:49.456640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.102515Z","title":"Regularization paths for generalized linear models via coordinate descent","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.102515Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:3b16065acdd9286d61fcdbb86db10b8da95e41be9eecf9f040971cd7360eebdd","observation_id":"dc222957-276a-4e96-8f35-48c64152adfa","resolution":{"observed_at":"2026-08-11T20:33:48.102515Z","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":"2026-08-11T20:33:49.439418Z","title":"Near-optimal sparse fourier representations via sampling","venue":null,"work_id":"7254e015-b0ec-4e1e-8e68-afcdb6c98c86","year":2002},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.106172Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ed5847890f52ec112c2eec8f98881bc1da2e83444df14b85be71839dc6926b81","observation_id":"5f8e5323-93c6-4c9f-866f-8428cdeda83b","resolution":{"observed_at":"2026-08-11T20:33:49.443687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.424952Z","title":"MLGL : an R package implementing correlated variable selection by hierarchical clustering and group-lasso","venue":null,"work_id":"7ec49217-dd10-49da-9437-fab80bf713e3","year":2023},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.109748Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:e9983bd58ec3b6b4d0c30e1ae529bdd8d29660113c710ec53609f198c7350a0b","observation_id":"1d4f072f-c12b-49c3-b497-164e0cb41cee","resolution":{"observed_at":"2026-08-11T20:33:49.429368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.404857Z","title":"Feature selection based on structured sparsity: A comprehensive study","venue":null,"work_id":"7f819fe4-3517-4214-b4c3-e2217a386d82","year":2016},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.113320Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:adf2b46c189e02340eceff6a02359446182d009fba4534274867f444d99acc95","observation_id":"43e3398a-2824-4a11-bd02-a2eb47f98bed","resolution":{"observed_at":"2026-08-11T20:33:49.413220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.387832Z","title":"Computing proximal points of nonconvex functions","venue":null,"work_id":"c95c279c-c9ae-454e-bb2b-42d8d9bbe00b","year":2009},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.117007Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:bdeba77b3cc41eb7e7f7aa00b0f038b2f0b60a800e268ffc5a2ed9e89edfb0b6","observation_id":"7ff7fe7d-e297-4d19-bbb7-4fc571cdd4be","resolution":{"observed_at":"2026-08-11T20:33:49.392392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07220","last_updated":"2022-07-14T09:45:36Z","snapshot_observed_at":"2026-08-16T09:31:34.586546Z","submitted_at":"2019-08-20T08:36:14Z","title":"A Bayesian Lasso based Sparse Learning Model","version":3},"cited_work":{"arxiv_id":"1908.07220","doi":"10.48550/arxiv.1908.07220","metadata_source":"pith","pith_arxiv_id":"1908.07220","snapshot_observed_at":"2026-08-12T00:16:22.474752Z","title":"A Bayesian Lasso based Sparse Learning Model","venue":"stat.ML","work_id":"a5905a0b-10c4-4a45-a8ec-3221da0a7dcd","year":2019},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.121173Z"},"links":{"cited_paper":"/paper/1908.07220","citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:e474449bb831ac36a73d188c7aa705a6c0635e134b9eea35bf086ff9893850b1","observation_id":"6c15ed6d-4abd-4b22-b096-36dd0ba75622","resolution":{"observed_at":"2026-08-11T20:33:48.435680Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.125272Z","title":"Ridge regression: Biased estimation for nonorthogonal problems","venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.125272Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:df3a74053ab4a87f50bc3964f9b048ace0301d8d3cfa50b5043a4827b8e704ca","observation_id":"7668aaae-7976-4f1e-8a17-6618a54b1fa7","resolution":{"observed_at":"2026-08-11T20:33:48.125272Z","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":"2026-08-11T20:33:49.369311Z","title":"Learning with structured sparsity","venue":null,"work_id":"be3714b8-fa6c-41b9-91f7-4bedfdef330b","year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.128658Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:a8fb3335fe2b8413fb4ad836f0b98b93ac88790a64c56ced056688c27ac27517","observation_id":"3ee88b09-c55a-41d1-a7d1-9b192c0118cb","resolution":{"observed_at":"2026-08-11T20:33:49.373379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.132474Z","title":"Hunter and Runze Li","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.132474Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:5c45bd19faa1e3f9758fcc0e53a9b47d83b246dd5cb0ef10d3996f6e71bd2b12","observation_id":"365bdbaa-eebd-4ffb-9c7c-2564ba42af45","resolution":{"observed_at":"2026-08-11T20:33:48.132474Z","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":"2026-08-11T20:33:49.359117Z","title":"Fast sparse group L asso","venue":null,"work_id":"e86e6d75-232b-4467-87c4-dd571874ef13","year":2019},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.136019Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:489c82307d30eb7e5f4e28f74bac150fced3268b20ba485f566203f8cf6df059","observation_id":"997509e0-ba64-4472-b043-2788070fc5cc","resolution":{"observed_at":"2026-08-11T20:33:49.362604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.347379Z","title":"Group lasso with overlap and graph lasso","venue":null,"work_id":"ba3c5127-8822-46c6-9083-e0b5c4fdc2cf","year":2009},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.141408Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:440ccf1d8e8cedee395a214238dc98bf162d07be751f5b2963ebea4e1335e2e3","observation_id":"7fe81432-a77b-4f3d-9698-c9e2fd0826a6","resolution":{"observed_at":"2026-08-11T20:33:49.351982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.333532Z","title":"Structured variable selection with sparsity-inducing norms","venue":null,"work_id":"6a843065-45ca-4aec-82ee-3a9a5aaac5a1","year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.145238Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:8595cae88fd73bcc20169331535df16f75d4d1c83e7a08e3c328535fdb43c03e","observation_id":"984c8d44-df18-482e-9f60-1614dcd7d956","resolution":{"observed_at":"2026-08-11T20:33:49.337368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.321729Z","title":"Proximal methods for hierarchical sparse coding","venue":null,"work_id":"e7b2262f-71cb-46a1-bdba-2d6e86f33615","year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.150039Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ee0e5f3af415f5880a60060b89f47f593980fb81974d7995ec5a4f4c22ea7773","observation_id":"952530d9-208b-41e1-aee8-fbbc074a7b9e","resolution":{"observed_at":"2026-08-11T20:33:49.325549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.155538Z","title":"Accelerating stochastic gradient descent using predictive variance reduction","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.155538Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:580e0c237c330f492c614f626a244db4040103b77e713297afd09c1d23fdaa2a","observation_id":"cf2d0b89-1156-48cc-aa51-1ff2bf212bfb","resolution":{"observed_at":"2026-08-11T20:33:48.155538Z","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":"2026-08-11T20:33:49.299076Z","title":"Self-adaptive lasso and its B ayesian estimation","venue":null,"work_id":"8e76187f-73ff-4bf0-a1c7-afb8c2de6394","year":2009},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.159274Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ffaf9adc4b2088705fbc9acfcc8af4a1a177a76915f34fc0adafa2d5a12cf163","observation_id":"5be62071-da5a-43be-9641-e01efca0e5a8","resolution":{"observed_at":"2026-08-11T20:33:49.303395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.286466Z","title":"Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eqtl mapping","venue":null,"work_id":"1039bfb6-60af-46e7-a9cc-e3f84f02464a","year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.163227Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:e856f674e020070542e8e2334b43a3970846c77cc1c640b758e4c4e17414e59a","observation_id":"e5d47034-4be0-49d6-8a59-7236d1fdab7c","resolution":{"observed_at":"2026-08-11T20:33:49.290190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T20:33:48.167586Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.167586Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:1ae6d4b2ec449573646924299759313d0647b8500e50f372355bb6903e83332f","observation_id":"0e553afc-1902-4a45-92bb-2e27c1a06fb6","resolution":{"observed_at":"2026-08-11T20:33:48.167586Z","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":"2026-08-11T20:33:49.274139Z","title":"Bayesian adaptive lasso","venue":null,"work_id":"c6a2fa01-52f7-4074-b952-723d52335f9e","year":2014},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.173054Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:cf239346bf174ce93b11a64a3c685de34a49410fae9ec569c8d7d1188a910644","observation_id":"5b8c0f4f-ca66-46ba-a48d-bcb67c3be6ff","resolution":{"observed_at":"2026-08-11T20:33:49.279167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.259986Z","title":"Global convergence of splitting methods for nonconvex composite optimization","venue":null,"work_id":"9461914d-a2bf-40e6-85e3-fb0185aa5e37","year":2015},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.176763Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:792c5c55c43c6dfc46c8e8a7384dbd0deb752220b3e9fd85a6534697d338c44e","observation_id":"73d14a97-8677-45c4-9de6-9337b1ac61ec","resolution":{"observed_at":"2026-08-11T20:33:49.264853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.246822Z","title":"Sliced inverse regression for dimension reduction","venue":null,"work_id":"d6904c5e-34fa-40e7-82d4-5d61ae2b6b38","year":1991},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.180401Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:061908dbcd92255130f1287d97bd5bab769258d2d1f9c32eb62b56e65464317e","observation_id":"64d2a408-bdd4-4547-9d9b-b2d564e6810d","resolution":{"observed_at":"2026-08-11T20:33:49.251648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.231226Z","title":"A simple sampler for the horseshoe estimator","venue":null,"work_id":"57e89ca8-c6ca-4056-8c3e-a5bdcb2ef3bb","year":2015},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.184452Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:74b0a02e84ef160ab076d1a73260d862b2c98729d2a2a0529d923e9c3b0cd039","observation_id":"408e70c5-8f77-4cbf-94dd-742eeb2c6c8f","resolution":{"observed_at":"2026-08-11T20:33:49.237715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.218240Z","title":"A new B ayesian lasso","venue":null,"work_id":"3a0e6a30-5a94-4abd-9b0e-62802542d063","year":2014},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.188332Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:fa9e9c722d1eba986d14873de2bc2f4b0975e2cddd114551c71d72fd488411ee","observation_id":"50198f04-f89c-4602-8977-a3ba00326045","resolution":{"observed_at":"2026-08-11T20:33:49.222175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.203730Z","title":"On exact _q denoising","venue":null,"work_id":"d7b520c6-4975-499b-a3dc-2e1c2e4df36a","year":2013},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.192152Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:6a9b768045107470e4452eb98df1b22b4c7a9572be74e9085212fed9c16c185c","observation_id":"771594f1-9a71-4047-a2b7-71782cf6687d","resolution":{"observed_at":"2026-08-11T20:33:49.207517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.189650Z","title":"Notes on cepii’s distances measures: The geodist database","venue":null,"work_id":"65fcf66b-781b-4179-86d5-e606b89db410","year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.197567Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:94573ab1aafe02323ce732978db9f743a9b0ff0046ea96a8aa4cd27448858dff","observation_id":"d1628d89-69a1-42c3-9beb-d01ff8d937d2","resolution":{"observed_at":"2026-08-11T20:33:49.193790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.202324Z","title":"Relaxed lasso","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.202324Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:f19b5196d68ad0eb41ef2ac34b3c2d40cc93edafb82534c486cc4e9cb016cc8d","observation_id":"172a3b94-4fbc-4cb7-a27b-ef00dea650a2","resolution":{"observed_at":"2026-08-11T20:33:48.202324Z","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":"2026-08-11T20:33:48.205991Z","title":null,"venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.205991Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:e979a7be58b1452f07f15d4d326ad0269af387850f3d708b1ed235eaae6046fe","observation_id":"4b97ad3d-63a4-454b-9fa1-f5bc49a4aaf5","resolution":{"observed_at":"2026-08-11T20:33:48.205991Z","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":"2026-08-11T20:33:49.166577Z","title":"Solving structured sparsity regularization with proximal methods","venue":null,"work_id":"b0978464-7752-4ada-8162-c1bdb921c1ce","year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.211174Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:0ac2999298eaa145e3a70a64f3d66a7cb216fb570ac834edc9e022d2d71256c2","observation_id":"40d3ac9d-2d34-4b33-9954-0c6d7464409c","resolution":{"observed_at":"2026-08-11T20:33:49.171082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1205.1240","last_updated":"2012-05-06T19:54:33Z","snapshot_observed_at":"2026-08-15T04:10:43.480980Z","submitted_at":"2012-05-06T19:54:33Z","title":"Convex Relaxation for Combinatorial Penalties","version":1},"cited_work":{"arxiv_id":"1205.1240","doi":null,"metadata_source":"pith","pith_arxiv_id":"1205.1240","snapshot_observed_at":"2026-08-11T20:33:48.619203Z","title":"Convex Relaxation for Combinatorial Penalties","venue":"stat.ML","work_id":"3e0dc64d-3166-4cf5-8cbe-a7565e988085","year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.216330Z"},"links":{"cited_paper":"/paper/1205.1240","citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:88395265e672579c134db1689b3f31dfe911d54c9f9dc3ec138b5628755119aa","observation_id":"9c3a78a2-b75c-4771-a40f-3db177640312","resolution":{"observed_at":"2026-08-11T20:33:48.624875Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1110.0413","last_updated":"2011-10-03T16:49:45Z","snapshot_observed_at":"2026-08-15T04:33:24.708625Z","submitted_at":"2011-10-03T16:49:45Z","title":"Group Lasso with Overlaps: the Latent Group Lasso approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1110.0413","snapshot_observed_at":"2026-08-11T20:33:48.220344Z","title":"Group lasso with overlaps: the latent group lasso approach","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.220344Z"},"links":{"cited_paper":"/paper/1110.0413","citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:6c3effbc33ca561f429d63f686d25588f7449422800fe0e3f8226d0e7ae584e9","observation_id":"c32d27ea-063e-45f3-a440-6d49bbc63a5b","resolution":{"observed_at":"2026-08-11T20:33:48.220344Z","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":"2026-08-11T20:33:49.153850Z","title":"Segmentation of ARX -models using sum-of-norms regularization","venue":null,"work_id":"2966e809-69d0-458e-96bf-377ee0061a86","year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.225035Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:88598ae3314e43783b168be839b8d17b1c97e92547f8f5688dfa97a96de371d7","observation_id":"4d4a61a6-f228-4315-b258-2daa523f0628","resolution":{"observed_at":"2026-08-11T20:33:49.158077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.143684Z","title":"Proximal algorithms","venue":null,"work_id":"307c7496-5446-4ca2-9e11-5769200d7a5b","year":2014},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.228328Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:d9951937786a8ab813cd78b6fd05e94de4404630f480913581daa5ae4b647f88","observation_id":"99d45f2a-5e02-4f81-8440-92f30612bc9f","resolution":{"observed_at":"2026-08-11T20:33:49.147181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.233130Z","title":"The B ayesian lasso","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.233130Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:7c5c807dee390014a1374f2984c384e08fd65b0331236506ce0a1b3879217474","observation_id":"0ec643c6-a0a9-47a8-b5d6-94d77c315872","resolution":{"observed_at":"2026-08-11T20:33:48.233130Z","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":"2026-08-11T20:33:49.133484Z","title":"Proximal algorithms in statistics and machine learning","venue":null,"work_id":"ac540b8a-37bb-4fc3-bd25-fa58acc6f667","year":2015},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.238481Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:0bd1483559da2865c2288a58683f80a15baa402c3f426f34c5c6762ad4ba2f49","observation_id":"5f9b5906-7d35-401d-926b-8260dd336df9","resolution":{"observed_at":"2026-08-11T20:33:49.136863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.241874Z","title":"Generalized approximate message passing for estimation with random linear mixing","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.241874Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:80734c5a13b5e5bdee3e2ba5a908fd2417f350f11231e158baea65f964664ead","observation_id":"4df69c01-296b-421e-9f23-6186e99558d0","resolution":{"observed_at":"2026-08-11T20:33:48.241874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1403.5074","last_updated":"2014-09-16T16:12:52Z","snapshot_observed_at":"2026-08-14T23:40:36.374434Z","submitted_at":"2014-03-20T09:10:35Z","title":"Convergence of Stochastic Proximal Gradient Algorithm","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1403.5074","snapshot_observed_at":"2026-08-11T20:33:48.246519Z","title":"Convergence of stochastic proximal gradient algorithm","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.246519Z"},"links":{"cited_paper":"/paper/1403.5074","citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:01c0e61ae5b86494ec639002b1388ebd8a635f5b225f2d5563114ee85db9a808","observation_id":"a4616724-15d7-414e-866c-9541bc8904d7","resolution":{"observed_at":"2026-08-11T20:33:48.246519Z","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":"2026-08-11T20:33:49.116171Z","title":"Nonlinear sparse B ayesian learning for physics-based models","venue":null,"work_id":"c5d5ece3-8b64-4b45-b8b9-f865b728b497","year":2021},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.251494Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:b16ba5ec69a97f2ba77b342ab3b6796c364e6388d45b072169adfcf62e092844","observation_id":"fb0619a8-700f-4c95-997a-be61a27d17dc","resolution":{"observed_at":"2026-08-11T20:33:49.119603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.103988Z","title":"Turbo reconstruction of structured sparse signals","venue":null,"work_id":"46ac130a-e360-4c10-8a40-7e358ca31758","year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.258137Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:c2d8d20c7260b862b3e11bd9002da7634cb570ed0ba2b8939ac52b41ad63adc6","observation_id":"ea89134d-6fc8-4dd6-9110-140d3b762a56","resolution":{"observed_at":"2026-08-11T20:33:49.108361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.263030Z","title":"statsmodels: Econometric and statistical modeling with python","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.263030Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ce6a52922be3be4070631e512c3dbf46bc25e0acc1fbc6b22776cf625e71777e","observation_id":"f8233ed1-912c-47f8-ae0b-83e587796757","resolution":{"observed_at":"2026-08-11T20:33:48.263030Z","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":"2026-08-11T20:33:49.083718Z","title":"Towards closing the gap between the theory and practice of svrg","venue":null,"work_id":"8b42c586-3726-422f-be05-187600327367","year":2019},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.267285Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:a324044f1ca045446b64cc05c540eee16389f5e89090f657139d0699942fc2ad","observation_id":"dea806f1-714e-4af0-a454-f5c198c4d616","resolution":{"observed_at":"2026-08-11T20:33:49.088473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.070713Z","title":"Learning the structure for structured sparsity","venue":null,"work_id":"49ea8d89-03cd-4245-b96a-80c3198f9459","year":2015},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.272108Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:128fa1d23775f25558b26585e9d1b2e49d95a70c6c85ec1a5ff563ff80caae0a","observation_id":"5e62640e-f326-4cd9-b2a1-d7a321160583","resolution":{"observed_at":"2026-08-11T20:33:49.075187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.059728Z","title":"The log of gravity","venue":null,"work_id":"cc862dff-1219-4cf1-a2ca-7a39c991f45c","year":2006},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.275991Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:31ed91d12041d53de6af0ef162bfa0d686609e28874edfd70059ae3f2ce7eedf","observation_id":"4904010b-d81f-4a3e-a200-23f9478c42b0","resolution":{"observed_at":"2026-08-11T20:33:49.063299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.279594Z","title":"A sparse-group lasso","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.279594Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:96b3836de206718313bc27279232fa1001e6f96626649b7acdeb5d36bc3bf80c","observation_id":"f2442c31-e0db-428f-8aae-925f074b2eac","resolution":{"observed_at":"2026-08-11T20:33:48.279594Z","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":"2026-08-11T20:33:49.042534Z","title":"Understanding the rationales and information environments for early, late, and nonadopters of the covid-19 vaccine","venue":null,"work_id":"93e9f6d9-3aa3-4f48-9879-35158038abf8","year":2024},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.284235Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:a1d08494a7d98fb285e0b1914648f15af74323ef483e03d9fd849ef0ebe657c7","observation_id":"6d17b7cd-519e-40d4-b418-4fd94fb447ee","resolution":{"observed_at":"2026-08-11T20:33:49.046833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.028419Z","title":"Feature selection guided by structural information","venue":null,"work_id":"87ebebfa-ce32-424b-b920-be6d86fc4278","year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.288185Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:5b7db8ed293c4a12343882fefcba66841419ea4b478df41d6148de4cdc28495a","observation_id":"ee57041f-67d3-422b-9868-e4754af6c7a2","resolution":{"observed_at":"2026-08-11T20:33:49.035354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.016895Z","title":"Deconvolution with the _1 norm","venue":null,"work_id":"8bc3df4f-bfc2-4545-9fa7-12dd88e0ac54","year":1979},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.292028Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:2771ac2b260d03ec79b96db09b5b751432983b07e879a23443a7bf7349d851e0","observation_id":"46ce2566-8514-4739-a7fa-b4d43fe615c3","resolution":{"observed_at":"2026-08-11T20:33:49.020921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:49.005157Z","title":"GPU -accelerated G ibbs sampling: a case study of the horseshoe probit model","venue":null,"work_id":"8ebcba10-43f1-481c-9e2b-e89b4f4a437e","year":2019},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.297797Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:4bf1831effcc4a46862dee767cd3a1b371896ceada1c6ee1baf8323897c35a52","observation_id":"51021bc1-1770-4b33-8a8c-e1e21267166f","resolution":{"observed_at":"2026-08-11T20:33:49.009051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.301445Z","title":"Regression shrinkage and selection via the lasso","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.301445Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:67934fbf07967d730a44c8106a96ad19a1042b35f25d5ad9e5805195cdb2f545","observation_id":"37f85609-e952-47f3-b55f-8dfe3882350b","resolution":{"observed_at":"2026-08-11T20:33:48.301445Z","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":"2026-08-11T20:33:48.993505Z","title":"Sparsity and smoothness via the fused lasso","venue":null,"work_id":"9f7bd558-cfcd-40a3-a564-016a7aa1849b","year":2005},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.305113Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:59d7a45bc13cbab04ab3ca00096f268cd97dde24ea20320c7e3a87513a8ccd81","observation_id":"ff638405-f323-4bed-a0e8-29da45022fa6","resolution":{"observed_at":"2026-08-11T20:33:48.997732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.981948Z","title":"Strong rules for discarding predictors in lasso-type problems","venue":null,"work_id":"c9783b8b-9f44-42b2-a400-d2828815817e","year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.308464Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:5b2f4e196e52277b8e23e02ff54bb54a50403504945f93e4c29f0b9fb3b1372b","observation_id":"879ef459-06cf-487d-846d-8bb0adc01922","resolution":{"observed_at":"2026-08-11T20:33:48.986309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.969997Z","title":"Tinbergen","venue":null,"work_id":"4d73b673-2a38-4761-a6e1-9c8afdc34762","year":1962},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.312107Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:643f0e2b79c92884b00a63187f62bfdead676cbb7d07bb2e24d0cb41377b8421","observation_id":"18dab2dc-d25a-4f49-a7b8-931b413a9918","resolution":{"observed_at":"2026-08-11T20:33:48.974124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.945310Z","title":"Sparse B ayesian learning and the relevance vector machine","venue":null,"work_id":"a65d5509-888d-438f-b651-859b09f78523","year":2001},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.315372Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:8c39f5f69e41210beaa4b4f3de3ec7f49669c9b8e78e0068c9f03bdbf4ffa8bd","observation_id":"72682a58-1b7c-4dde-87e0-551e8c4e8dbe","resolution":{"observed_at":"2026-08-11T20:33:48.948775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.934474Z","title":"Deep active subspaces: A scalable method for high-dimensional uncertainty propagation","venue":null,"work_id":"5e720c00-6bc8-46d8-b577-55b396489883","year":2019},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.318514Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:8c53bc54ecc4a9ad74b2e7f9b2cf96768de623bece10135f1c5b2813861e9144","observation_id":"f8ab26a2-e9ca-4cea-957f-d4017fba8b5e","resolution":{"observed_at":"2026-08-11T20:33:48.938130Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.923998Z","title":"Experiments: planning, analysis, and optimization","venue":null,"work_id":"955d3481-5c96-439c-b132-9a1dc81f2952","year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.322187Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:a1138e23b14df51fbad63cba4adb66a360976a15eaab533865125b8c7d99b235","observation_id":"3b837670-69d0-4c9c-b3c1-6c3c28927b22","resolution":{"observed_at":"2026-08-11T20:33:48.927991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.911228Z","title":"A proximal stochastic gradient method with progressive variance reduction","venue":null,"work_id":"0242ef16-5661-4048-bada-e7ea5ed5a109","year":2014},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.326067Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:b537d01e51783fa740bb23342ce035047fba0875192aa68087ed66a95f8dad13","observation_id":"31851076-255f-4cab-b64f-da4d1365ceec","resolution":{"observed_at":"2026-08-11T20:33:48.915539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.899237Z","title":"Efficient methods for overlapping group lasso","venue":null,"work_id":"b67ea747-a9e7-45e4-928b-869859f6601a","year":2011},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.329709Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:93a02d388234494c8eaef0433302569c7001053e5c69043a52fec0171bf14c34","observation_id":"1c259cc2-818f-4e6e-a8f1-3cca4dd4d3d9","resolution":{"observed_at":"2026-08-11T20:33:48.903222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.333455Z","title":"Model selection and estimation in regression with grouped variables","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.333455Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ea1bd964a623fb27c3ad7fc1689c5fc749c45d6c6b0e6b705291eeb7e488b889","observation_id":"1588da60-efbe-4e7d-93c6-857bb23e967b","resolution":{"observed_at":"2026-08-11T20:33:48.333455Z","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":"2026-08-11T20:33:48.336515Z","title":"Nearly unbiased variable selection under minimax concave penalty","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.336515Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:7e3bf0dd369be37ae70e00eabd1b27f7e8dae7886e3cb0c99992ba11cc952320","observation_id":"7fcfcc49-42da-4f41-86f7-da9a757c1649","resolution":{"observed_at":"2026-08-11T20:33:48.336515Z","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":"2026-08-11T20:33:48.879708Z","title":"Bayesian group factor analysis with structured sparsity","venue":null,"work_id":"40852121-2f03-494f-b285-7f10e881b780","year":2016},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.339543Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:ffef4c08b1d6034b3f1edd68bd982277c4cc31224574d4a5400010390bc67359","observation_id":"d63f78d6-80c1-4783-8e3c-d1e7a6b1748c","resolution":{"observed_at":"2026-08-11T20:33:48.883665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.867537Z","title":"Modeling disease progression via fused sparse group lasso","venue":null,"work_id":"d7df5bb9-ebfe-4f94-ab56-7a52ac91b266","year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.342324Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:7950c539599080fd1132cbbd6ed33e170c7ff30768525026bd92042d39a4db0b","observation_id":"677d15d4-f091-4f71-9dd8-2e9ec1a18ca9","resolution":{"observed_at":"2026-08-11T20:33:48.871597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.854919Z","title":"A generalized framework for learning and recovery of structured sparse signals","venue":null,"work_id":"86c0637e-3a32-4925-af0e-1da8ec073f2b","year":2012},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.345145Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:45b666901f4aca341464343176760e1235954de9408e00997c5b9d46fcceaa4c","observation_id":"4ea21349-56d3-4d5e-b534-436c64d03d27","resolution":{"observed_at":"2026-08-11T20:33:48.859496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-11T20:33:48.348298Z","title":"The adaptive lasso and its oracle properties","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.348298Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:6f3079fdf9129300fb8782e1b5dac8087ec5079f2ab8d6dc1481f9956762eda5","observation_id":"5e82a26e-759f-4a41-8ec4-9c49be7ff804","resolution":{"observed_at":"2026-08-11T20:33:48.348298Z","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":"2026-08-11T20:33:48.352086Z","title":"One-step sparse estimates in nonconcave penalized likelihood models","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.352086Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:a161f2f1242490eee3d04035f3d925596a6e65138da3f516e05de64cdc4ebfa8","observation_id":"c69d1d90-58ff-4bf2-a41e-48718668983f","resolution":{"observed_at":"2026-08-11T20:33:48.352086Z","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":"2026-08-11T20:33:48.356510Z","title":"Sparse principal component analysis","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-11T20:33:48.356510Z"},"links":{"citing_paper":"/paper/2412.05726"},"observation_digest":"sha256:44e3127ac61cfff98260df11cbc3aec93b0114188fd0a44f61ceeef71c7cfa68","observation_id":"1eabfcd0-caaf-451b-b14d-bd79ef22b343","resolution":{"observed_at":"2026-08-11T20:33:48.356510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.05726","last_updated":"2024-12-07T19:19:55Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-15T12:05:25.395225Z","submitted_at":"2024-12-07T19:19:55Z","title":"Proximal Iteration for Nonlinear Adaptive Lasso"},"reference_resolution":{"displayed":85,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":4,"verified_fuzzy":51},"total_outbound_references":85},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 0 inbound Pith citation observations for arXiv:2412.05726."}