{"as_of":"2026-08-21T17:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8b7ed20621a35e41243492a52ec8f20a5459a1812ed089de935386796cda9b30","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":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/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":"2026-08-19T04:00:13.907814Z","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":"sha256:8d0d7441d5bf3e39de13f55f241551daf59953a5401392b3ad0776c38daae4df","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":"2026-08-19T04:00:13.907814Z","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:bd07a49b51ccbf49875c9af10f335ae256e39e1fffb24434f6e5b56743bf0c0a","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":"2026-08-19T04:00:13.907814Z","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":"sha256:6f19bc7ce57635b04e13f1a830f975afd4d7d4f15969bb09d2c07e7ec81d6e5e","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":"2026-08-19T04:00:13.907814Z","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:ee3c29923e1a7ebcb06a1fb0b128a90df6118b50ad7092d3df88b28421a7288e","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":"2026-08-19T04:00:13.907814Z","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":"sha256:6c1102a2ec7812af563b577923b23497d9e0f151c81e7c95fd9632a4a73886a3","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":"2026-08-19T04:00:13.907814Z","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":"sha256:1fea85ce9abc5bbd91f994480fd707af15cb3a2fe33a6d8c87721081add6bccf","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.567821Z","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":"2026-08-19T04:00:13.907814Z","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:b1a461c3289552ed7cb7e003995b5fcc2dbc8c50b86b17b0eb76f06cd61e1b14","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.557432Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:3d164d7a69eac3f68662734df83c06b39541b65baabdc97fc7f735e4bc91f5bb","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1212.6088","last_updated":"2012-12-25T22:02:47Z","snapshot_observed_at":"2026-08-19T13:07:22.285965Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:379b63f126b2afcd77957afdf2141953c35158d005d97ac6dc301a849a813c96","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.045020Z","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":"2026-08-19T04:00:13.907814Z","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:bca7a304867d5621f8471f2ca2fe6c6f98a93b2e1aef98dee38133a75bf90941","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":"2026-08-19T04:00:13.907814Z","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":"sha256:0c81f5d1bf392b92cc60afae27933abe75493428dd51fd2a3e4991379dbdb1f7","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.536710Z","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":"2026-08-19T04:00:13.907814Z","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:a34c8e6306bec6c40913b16df1b80f66ced8b4fae924c5c08870fc3ab8b10eab","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.525476Z","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":"2026-08-19T04:00:13.907814Z","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:e4c3712540cae98ce9961e2755d5c0a02715fa1fa9309c4e824c58f8724741f9","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.068140Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:36930e2232fd7ed3c5df17b5d568ca68e4babfa3ac2f4e0ea5c4dcd04b1212ba","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":"2026-08-19T04:00:13.907814Z","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:d7c863f2988c88d5a5fc2a6ba085cbd100c876240af52d02861971a796741149","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"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":"2026-08-19T04:00:13.907814Z","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:b8ecd1d6d5a84a6ce07b37791d45acc7bfac4359ac9ea79256083fc16dc1930e","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":"2026-08-19T04:00:13.907814Z","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:b4648d0463d4286efbe9e4109559e267b7e0e6357fdb2a085693dec516f99134","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.488020Z","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":"2026-08-19T04:00:13.907814Z","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:dd4cdf79c86d4b48958baf50c1013eac09ae246dee925106188c0ef7ff96f925","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.472519Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:26c3911f2caae1773209a524f5e226cac87d95b727a94d773eba026c8c75f01a","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.090939Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:8a781469aa1cfcaae777f651c817fff2fd7d77b44d7038d5d78b83623e69ae42","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":"2026-08-19T04:00:13.907814Z","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":"sha256:912d33ef829c7e29fe193c178d2405daa188a2b4ee0e20b14f13360f33980974","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":"2026-08-19T04:00:13.907814Z","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:a9f110727fbb379059f86f37c668b4c9d407600cd77b634a8d25a5a4415a1477","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.102515Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:599f9ec6b93a6754be1e7d9fc75b324b62314b323b435358767701c980aa5ca5","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":"2026-08-19T04:00:13.907814Z","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:e3fa8d2e7a0d6b4a9c90a1e93570006fed427993e9ca3df94f40cd9ec4b0c82e","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.424952Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:4188d424274f672d65162fe76554c59d7fec6b1257e543a673257c18ef6d98b6","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.404857Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:16f214dcc9a17d8c3fe81bf1cbe76a786a4da48bd77c5d89b895919d68c526a4","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.387832Z","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":"2026-08-19T04:00:13.907814Z","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:b1e103e7d92f041533bc09ec1d27e416cc63e462b9a7d77f9074f2ed875090b7","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.07220","last_updated":"2022-07-14T09:45:36Z","snapshot_observed_at":"2026-08-21T13:19:01.372902Z","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":"2026-08-19T04:00:13.907814Z","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:c2408002e18130b1f1f8fee853c8e1e17784545eb65ecfdb8259bf85d4099e30","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.125272Z","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":"2026-08-19T04:00:13.907814Z","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:aac5e152b92e937088a6b2d94e5b46607f03c403d4165fd7e321eab6817d2349","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":"2026-08-19T04:00:13.907814Z","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":"sha256:931024c9434382ad3d50015a630f2fa3d16da148eb817ae8eb0ff9d17da622e6","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.132474Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:3a63f043c6c1aef23d2d8bbf13b27403459cf2e8eca73f732bc2b6c6bc779d36","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":"2026-08-19T04:00:13.907814Z","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":"sha256:0716a6863268d450c75bfcf370b1062a3a11c96253e3c961e4c5212705cec816","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.347379Z","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":"2026-08-19T04:00:13.907814Z","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:ed732e6fe1ff791e83df11e72e7c258170efc22fe064eff8fad9364d109ff275","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.333532Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:9a26531cbf874c9befe7187f9597d2ad8ba312c8237a09dfb17cac673cba1add","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.321729Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:32d7340f885aa63c8e21cbe2e1aa6d46b3a7ef4d7d5fcde575db5c2fc0dcc363","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.155538Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:83f0f73f6cc5b15b48b159b0698782500f9aa3a9597a8bc6434c3f22ec09e0cc","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":"2026-08-19T04:00:13.907814Z","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:bc3c179702b780711c08d6db0dbf3ca90a9c42ef20d8a6fbf43f2cfe3609ae2b","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.286466Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:4ee78a7d5fab8e68993c584998466dbdbe84e503c6ffc993c18198a73e49dd5f","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-17T19:26:44.032537Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:6f98b07073b173605885cd132e141982145f2abd3e1166b4105d8ccdfd52c173","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":"2026-08-19T04:00:13.907814Z","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":"sha256:22070b2861cb5252717d838371272ee1fae1ce93e58459bae55b9b1e40d2bbe4","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.259986Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:4a0b4a7c47b6dde1557a554a19a8dccf91568c920ba4df408df30d8eb6b3689f","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.246822Z","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":"2026-08-19T04:00:13.907814Z","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:c9f5261df1af406f3b34f2f3b17fdb1bc25d8e7a307f10675728d6515c3c02b8","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.231226Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:07ca63cc1d9e9eb571e8850ad5a504e6cdca8d2befbc93a10f99f40fce8f838f","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.218240Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:3b1464cf3d4d33ceffa0cdfea410ded4220a76bfde4f29dc7f51d8fc2df96ca6","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.203730Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:2daa12f925dd2ccfad027bd09f27a7f5dcb9ec16c84ab8e432740ca417892d6f","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.189650Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:13ac0d36569c6225e19106ce6bfa86cf2a04edf0dd7859f371793b7abeaecd16","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.202324Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:3b9ad5cc452cbc61692884003957804a1c77560794e816b9fa012e1322f6d464","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":"2026-08-19T04:00:13.907814Z","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":"sha256:5a292f266c6a594509968e41316cbf6775ad19ac1dd9678dcd306417884e0686","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":"2026-08-19T04:00:13.907814Z","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":"sha256:6ee3478ca91450558c9fb910449bb2e9539d1e14ba9b82210ae5a4e7d472c71d","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"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":"2026-08-19T04:00:13.907814Z","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":"sha256:38fb00cab3407274e953c9bb5cec70038c25daae63b2b4b54a963f1b716f789a","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1110.0413","last_updated":"2011-10-03T16:49:45Z","snapshot_observed_at":"2026-08-21T00:46:15.769264Z","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":"2026-08-19T04:00:13.907814Z","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:acb29f9e784480de20028c5e13f7e63d491368623b8864c50eb7e53ec82f84d1","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":"2026-08-19T04:00:13.907814Z","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":"sha256:17b253c329ad50c9b7d0991aaca9c8ac81850622fabb766ec182748f0c12ea92","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.143684Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:64b4850a2757814954063b8a1561d3136d3acd3fa56db4e8a83ae75fe1676e1c","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.233130Z","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":"2026-08-19T04:00:13.907814Z","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:db7fb9c7b619816754ce8574fb5633b9013157df593142adf521d21c8d9dcd79","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":"2026-08-19T04:00:13.907814Z","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:accca1e88df6cb493c67698180238970172527141a51e32e510e9e04ffe04602","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.241874Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:1c18a7762de98eae6e7e0543936ebc4e16a693cfbca4c323a27da36d00e97099","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":"2026-08-17T12:00:56.668775Z","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":"2026-08-19T04:00:13.907814Z","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:ed6e85cec71861ac106702ef959753d56ffdd7c5de855f2e605c61d617d4bf7a","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":"2026-08-19T04:00:13.907814Z","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":"sha256:8f2bb950cf4dfe070d069326a5e4214d3205ff0f9dd5be1d6092135db0d2957c","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.103988Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:91b663d6d65d8cbb7ff2c9b626eb92d9f34f42962f2b644bb1876761b745773c","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.263030Z","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":"2026-08-19T04:00:13.907814Z","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:ab2cd633aa2eb5205d95742fd2dfe28abfd6d0e369cb6cb0aa612dcfaa079e6d","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":"2026-08-19T04:00:13.907814Z","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":"sha256:57c9162c49499596a7dd69a1064608fa946fc7ec353e08ab2371353d0696d662","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.070713Z","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":"2026-08-19T04:00:13.907814Z","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:a1a29da9e499f86b5b2705d4be5c4f9363eb9c5e0f4b5d7d31332b746115986b","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.059728Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:4ece019156badf9ce37711b72537bb53194e7810ddd904f5bd06d413b32bf712","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.279594Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:604c5b941d1da67ada60a3896e04014255493703ee940697f03b07759f5e2a1e","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":"2026-08-19T04:00:13.907814Z","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:b336fcae0c6c30fb407039e42e08b6cae5a6c54cef517bcbb6b1e19200445544","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.028419Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:906c15adece8504b793c08edbdd7db143cb548188340ddd005ecaeec0f76aa62","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.016895Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:5aaaa0c33d5112091dd59bbf496444fa3d2e76243f452c70ad2f71e547b0d13f","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:49.005157Z","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":"2026-08-19T04:00:13.907814Z","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:ee5a0c8945a43df6d9d65c1040e176c5d4b90ef11d8a5a5b5eafdf483ad906ef","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.301445Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:8a16331c5d50ea2e8e6baa39e03e6c909fd4b515fbfe9db61882d546290f2322","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":"2026-08-19T04:00:13.907814Z","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":"sha256:250a74f36f75a012434ab63d340daed256a2544f514ddf46fb31cdb630ba5590","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.981948Z","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":"2026-08-19T04:00:13.907814Z","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:ba43775b5f5e1f904da4ed47240df51ed86b2b69117ad925dd33f4bf3e2ad68b","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.969997Z","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":"2026-08-19T04:00:13.907814Z","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:b991c79174fb21bfd4bdfd14996adcce81f5a9c34b2c3a5cb38e778d149abc0c","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.945310Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:04b2d48b52663166196b063cc47758b0d40671eb61366a818093dca71df55328","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.934474Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:56d28ebd10a2db299f0af2b616ac4c1059dbc78421f3678a99f4e51d859af8d0","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.923998Z","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":"2026-08-19T04:00:13.907814Z","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:b706a15dcf0853fd6f5d1b80cd1f447b73d3ac6305520063f02af390ab4810b5","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.911228Z","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":"2026-08-19T04:00:13.907814Z","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:bcf1bd71658dd672574faa1187a7b3fb9cde08bae71ff2f2b49362b6be49c245","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.899237Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:5188ed19662ed433859877cb0dc2675506cd4b9099e165b1f7a26a0a340a0db2","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.333455Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:1325bd13c6e84d39239ddfc01c6c78e5611889059bbacd39cded3fcbec882ff7","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":"2026-08-19T04:00:13.907814Z","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:cbc8b62c4eaac16866dab40c7aaab5233158b394462e8d790419e2879392dd82","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":"2026-08-19T04:00:13.907814Z","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:a582b0ba473e3b42f0dc997b62630fe618520a56a599b34d6f06bf4622cd0fb0","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.867537Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:9e426adf54d17c3d121dc1b0952acf0afc3183ad434dd89ef8440fd18d23cb16","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.854919Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:003e2832c1292789772985ecd7310055fb853f6d45e3b7effb9cae47876cbb0e","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 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T20:33:48.348298Z","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":"2026-08-19T04:00:13.907814Z","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":"sha256:8124484cb05e567d1a3bba7b004d4b811179ed30d7df3504c4d52d6992c6b471","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":"2026-08-19T04:00:13.907814Z","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":"sha256:2f436688939cef59d0a68fa33b5833865b5a1c3ee75522b844c48ffdc6f6187f","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":"2026-08-19T04:00:13.907814Z","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":"sha256:158a20e9ff9a1df619df70c0929b23b74ce493c6583f437708e22671756a1b91","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":"2026-08-19T04:00:13.907814Z","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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 0 inbound Pith citation observations for arXiv:2412.05726."}