{"as_of":"2026-08-10T00:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2031a41515f6809c1d8cf80bc9109c3d0f7121ce0d7ff5d40de1f706e8accf91","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:04:08.460029Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2505.16829/citation-record","integrity":"/paper/2505.16829/integrity","json":"/paper/2505.16829/citation-record.json","paper":"/paper/2505.16829"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:04:12.693901Z","title":"Taming the monster: A fast and simple algorithm for contextual bandits","venue":null,"work_id":"e1986ec0-7879-4eab-82b8-a610b65dd8e2","year":2014},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.224888Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:13f805e68d1aa223eb8c2533e6bdfac3ca013dbecbb2cab984043ded4a0a77ac","observation_id":"ba893858-a2f1-4747-a77f-500942ab4e20","resolution":{"observed_at":"2026-08-07T15:04:12.707621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:12.625554Z","title":"Semi-bandit learning for monotone stochastic optimization","venue":null,"work_id":"201d5c63-cee7-41b6-8a37-582e3cedfec9","year":2024},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.306072Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:e8a70b67a157913824fac0a5638c60c726c3aa78d9007a7b02830f8119290250","observation_id":"71c1f5ea-7802-4d9b-98f9-d2b03e3f7fe4","resolution":{"observed_at":"2026-08-07T15:04:12.653802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:12.537930Z","title":"Contextual pandora’s box","venue":null,"work_id":"7c7da71d-88be-497a-aa55-44a904e9742d","year":2024},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.390919Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:083bd827b6cc92c0ffabf98b4e6a1aebf40e695af558cdbcf48812cf51328591","observation_id":"e2419198-beb3-4760-90cc-2e4e573743c1","resolution":{"observed_at":"2026-08-07T15:04:12.584696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:12.455880Z","title":"Multi-item mechanisms without item-independence: Learnability via robustness","venue":null,"work_id":"c6587535-bda2-46a0-8966-2fc3c9ac354b","year":2020},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.493541Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:d0534874faef59f770ffc2108239711bf01a50cfb95c6329284ff667617e8ba9","observation_id":"24f0dcb3-0857-4003-baf1-8a7b0918e39d","resolution":{"observed_at":"2026-08-07T15:04:12.493232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:12.376493Z","title":"Nonparametric pricing analytics with customer covariates","venue":null,"work_id":"f6bbfbca-61cb-42bc-a4e1-aa528b9b04db","year":2021},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.580969Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:6661bbb89cde3c73c3fded4a486b567b09549db2fe7f76f754ace6e5287a44ff","observation_id":"66541f08-2550-45a4-9ad0-3f7eb20c6607","resolution":{"observed_at":"2026-08-07T15:04:12.416443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:12.300255Z","title":"Contextual bandits with linear payoff functions","venue":null,"work_id":"f12d449c-bd74-4a48-803a-d8078fd3564f","year":2011},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.680263Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:a042bb6b8d99ac6310f77b9c1cbd2c72b140716516bdd032df438ca98994b2a1","observation_id":"604b8b84-ba73-4a04-808b-4ebd76d51a71","resolution":{"observed_at":"2026-08-07T15:04:12.342906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:12.159927Z","title":"Cohen, Ilan Lobel, and Renato Paes Leme","venue":null,"work_id":"d17896c4-fab7-4f4e-9f8a-9cdf91862388","year":2020},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.766503Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:7f95ad40024939de909efb80853c65240f2cb607662ff42460f35165e6cc80aa","observation_id":"7b0a4a27-619a-4d64-be38-83f1d7404f5d","resolution":{"observed_at":"2026-08-07T15:04:12.209175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:12.069153Z","title":"The sample complexity of revenue maximization","venue":null,"work_id":"b1893b0b-ecce-4728-9b3d-b0d667fc41ba","year":2014},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.848385Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:a9cb58200e5a2469bf1f227a4ce88bd72aac621eb1a05de33575d0e06258f008","observation_id":"17eb02f9-81a3-4ce5-8356-84275b67272d","resolution":{"observed_at":"2026-08-07T15:04:12.145841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:11.895552Z","title":"Prophet inequalities for independent and identically distributed random variables from an unknown distribution","venue":null,"work_id":"ca1cb4ef-c824-42df-95ad-b05957f95cfd","year":2022},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.937983Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:3bf9492ec5c5eaa69718413e7187a0b63b91577fb4ea8855b35b6fbba01f1f53","observation_id":"bfccfe50-36f5-4462-a6b2-8b0680ea850c","resolution":{"observed_at":"2026-08-07T15:04:11.971477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:11.775522Z","title":"The sample complexity of auctions with side information","venue":null,"work_id":"5c9acc15-6681-4232-bd9d-327812adedfd","year":2016},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:06.990880Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:f3b5638f1a112e1739e0905ca96e0ba62b48e81a38d5d0f1546e01d350f7edc6","observation_id":"64fc9806-31a8-4ce5-a061-5d996a1f37a5","resolution":{"observed_at":"2026-08-07T15:04:11.818382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:11.636959Z","title":"Efficient optimal leanring for contextual bandits","venue":null,"work_id":"6aaa4c52-a8d9-4703-822c-1272357c88d0","year":2011},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.060155Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:eef87e92eb0cd2c0d7cdc61561f37fe59462968d67030efeedd5ca8177a0b735","observation_id":"9fa99486-84ec-4408-a5d4-dc6c9e666b29","resolution":{"observed_at":"2026-08-07T15:04:11.706089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:11.501852Z","title":"Posted pricing and prophet inequalities with inaccurate priors","venue":null,"work_id":"68c6f325-4dbc-4574-ab25-d858aca5426b","year":2019},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.164398Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:9c6b702dd89aa39d3d84d66e691c06dc30444b9f9178e8aa55be18c093cb2a6b","observation_id":"8474fd59-2328-409e-8d4c-5390c1aa3b7d","resolution":{"observed_at":"2026-08-07T15:04:11.572431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:11.330939Z","title":"Practical contextual bandits with regression oracles","venue":null,"work_id":"fc801ef6-52de-4d11-aa9f-22990f6d1b11","year":2018},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.262706Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:dd3566df244d7955aa871632058a60931b3212a068c174e90fc32fe968b26c2a","observation_id":"69fe6b44-ca47-437d-ac6b-4f092eb7d2ec","resolution":{"observed_at":"2026-08-07T15:04:11.399287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:11.260289Z","title":"Bandit algorithms for prophet inequality and pandora's box","venue":null,"work_id":"1ab4dee7-64e8-4a8c-843e-f3a1945c0dc1","year":2024},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.319928Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:4fbf847f198a1c8412b14e6dc0ece55210f32f0ebd2dbe63fb682728594faf13","observation_id":"cf379dee-b170-463c-9614-64c1e8ea623a","resolution":{"observed_at":"2026-08-07T15:04:11.281715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:11.102178Z","title":"Online learning for min sum set cover and pandora’s box","venue":null,"work_id":"9ff28ba5-be72-4ec4-9d99-c58b4e5e7a93","year":2022},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.378413Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:821ecb726306c6ac08cbf6371240a4a19f8d62e8d74bc637bb64f457d24fc84e","observation_id":"f8d2e820-fad7-4d0b-85d8-771c1be56d4b","resolution":{"observed_at":"2026-08-07T15:04:11.168464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:07.438125Z","title":"On choosing and bounding probability metrics","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.438125Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:5614d733b5f9ea28c8e142116f02793e27ac65ff879778c98f2293c44c52da6d","observation_id":"319665ef-1851-47cd-958b-4ccb6118b0de","resolution":{"observed_at":"2026-08-07T15:04:07.438125Z","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":"2026-08-07T15:04:10.962216Z","title":"Generalizing complex hypotheses on product distributions: Auctions, prophet inequalities, and pandora’s problem","venue":null,"work_id":"c4a596c9-040b-49f1-8434-5641b5a0791d","year":2021},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.499022Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:be3b2a8f8fa603981177210465665c0ef120ce89a314ded5641cc155c52507eb","observation_id":"f63aabd0-1449-4f0b-9905-28c793af223a","resolution":{"observed_at":"2026-08-07T15:04:11.014023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:07.599840Z","title":"Probability inequalities for sums of bounded random variables","venue":null,"work_id":null,"year":1963},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.599840Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:98e8d27fd97b39e536c030cc80d5ebc1c3da66a7d1c80dad788367a47afa21f5","observation_id":"2bc15d16-af47-4c35-bbbb-fa5c35e4a0b1","resolution":{"observed_at":"2026-08-07T15:04:07.599840Z","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":"2026-08-07T15:04:10.775717Z","title":"Dynamic pricing in high-dimensions","venue":null,"work_id":"7d1dbb8e-a707-4175-9582-da17ff40e175","year":2019},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.673515Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:a280d987714aa8e7d9c1981d0cb2c56ad0c27650a95f59a033ed347c42e9ae10","observation_id":"117bf9fd-d77b-4f41-bc42-4bb527d169bc","resolution":{"observed_at":"2026-08-07T15:04:10.837060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:10.727509Z","title":"Sample complexity of posted pricing for a single item","venue":null,"work_id":"eddb4786-b5fb-409b-998a-2808d4711460","year":2024},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.752507Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:6b49107fab638c5127382ec0e41e44b0a58b67cdc30e3332f502357dfbabd328","observation_id":"0a0be16e-240e-4a24-98e9-57052a3b918b","resolution":{"observed_at":"2026-08-07T15:04:10.759602Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:10.474024Z","title":"The epoch-greedy algorithm for multi-armed bandits with side information","venue":null,"work_id":"f116b97e-cbf7-42a9-b523-acbea8ded39c","year":2007},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.846511Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:cca2c81597ba24018cb7c1c19f00c23a20362aa31cf14807b79630bafbf64d45","observation_id":"fa404ec8-d619-4a7b-8810-bab4b15d4fa4","resolution":{"observed_at":"2026-08-07T15:04:10.618895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:10.212386Z","title":"Distribution-free contextual dynamic pricing","venue":null,"work_id":"e6c3b155-866a-47a2-b701-0aab5532b910","year":2024},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.921986Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:7fa77cbcf657139c976aadeb684bce765530cd9796be82862a7b1795f9b2d269","observation_id":"93b849b3-bf1b-44ae-9ad1-be32fadd313c","resolution":{"observed_at":"2026-08-07T15:04:10.327162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06918","last_updated":"2015-11-21T19:48:40Z","snapshot_observed_at":"2026-07-06T04:37:20.777239Z","submitted_at":"2015-11-21T19:48:40Z","title":"Ironing in the Dark","version":1},"cited_work":{"arxiv_id":"1511.06918","doi":null,"metadata_source":"pith","pith_arxiv_id":"1511.06918","snapshot_observed_at":"2026-08-07T15:04:08.883370Z","title":"Ironing in the Dark","venue":"cs.GT","work_id":"ab43a8a1-3033-444f-81b7-fd3cf60b89c1","year":2015},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:07.993104Z"},"links":{"cited_paper":"/paper/1511.06918","citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:eefc205f5434a9068ae4e93656d43fae035eb3ef576f0cf30f3336bdd8dfd47f","observation_id":"7e327a61-244f-4dfa-a6b3-0bb90f27c72b","resolution":{"observed_at":"2026-08-07T15:04:09.008170Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:09.916768Z","title":"Learnability, stability and uniform convergence","venue":null,"work_id":"78cca0c0-6c8b-4d64-bc0d-9d8940aadabc","year":2010},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:08.096549Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:17f6eb6ae6b883230bef79fa71caf651af3f81691b5908412792baf0a3eb13e3","observation_id":"4521303f-b6db-4482-aebc-18b35cd93ca7","resolution":{"observed_at":"2026-08-07T15:04:10.035376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:08.162193Z","title":"Bypassing the monster: A faster and simpler optimal algorithm for contextual bandits under realizability","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:08.162193Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:179c9926d74f9ae65abf678acd0ee3669ff2c1cc827c288af31f8515c5c72e5d","observation_id":"32fdd793-ffee-45f7-ba4d-648d7379fa94","resolution":{"observed_at":"2026-08-07T15:04:08.162193Z","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":"2026-08-07T15:04:09.690490Z","title":"Contextual bandits with packing and covering constraints: A modular lagrangian approach via regression","venue":null,"work_id":"5aa8c63d-05f5-4759-b339-cc8646c8817b","year":2023},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:08.235208Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:c9319deb4ef19fb1613f3c83c4a857853e4375dbcd40fffc1b09b1cb4ed3c589","observation_id":"b09ef69b-bb59-48c1-afec-ab0a4b48cd47","resolution":{"observed_at":"2026-08-07T15:04:09.781344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11316","last_updated":"2025-02-12T17:28:17Z","snapshot_observed_at":"2026-07-06T18:32:02.206165Z","submitted_at":"2024-06-17T08:26:51Z","title":"Improved Algorithms for Contextual Dynamic Pricing","version":3},"cited_work":{"arxiv_id":"2406.11316","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.11316","snapshot_observed_at":"2026-08-07T15:04:08.626978Z","title":"Improved Algorithms for Contextual Dynamic Pricing","venue":"stat.ML","work_id":"983d3ecb-429b-48e8-abdf-3e89cd725a20","year":2024},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:08.331599Z"},"links":{"cited_paper":"/paper/2406.11316","citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:b9dc64dc9cbbd26c2b30ece797837b3a4507fe7424b018ebe0a5356f14406288","observation_id":"8af057ab-f41c-41a6-8f90-8da824c29c4e","resolution":{"observed_at":"2026-08-07T15:04:08.756614Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:09.422573Z","title":"The nature of statistical learning theory","venue":null,"work_id":"b051e248-6b8b-4827-b08f-59571beffe26","year":2013},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:08.403587Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:6f479dc05ef6c30b91a1e7e714858581aba2827286e04adab0935a7718d76f36","observation_id":"ca0f23e9-5f65-4828-a957-d45b030f12bb","resolution":{"observed_at":"2026-08-07T15:04:09.527489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:04:08.453247Z","title":"Weitzman","venue":null,"work_id":null,"year":1979},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:08.453247Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:378cb4ea66d563dd77c19cd55284aa11d04d7983069d7747b915ec3801d53d59","observation_id":"8668bfa3-5bbe-40d4-805f-605e7e78421c","resolution":{"observed_at":"2026-08-07T15:04:08.453247Z","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":"2026-08-07T15:04:09.153842Z","title":"Towards agnostic feature-based dynamic pricing: Linear policies vs linear valuation with unknown noise","venue":null,"work_id":"920d70e4-e5cd-48e1-906d-5132ae4f59b5","year":2022},"citing_paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T15:04:08.460029Z"},"links":{"citing_paper":"/paper/2505.16829"},"observation_digest":"sha256:bf8afaf2ef4b07a21fe607bf591a353882528ffe8e7c1c06cf5e4c8e67239c76","observation_id":"a42e832f-fc90-405a-a34c-661d816b832d","resolution":{"observed_at":"2026-08-07T15:04:09.265381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.16829","last_updated":"2025-05-22T16:01:49Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T14:52:14.607882Z","submitted_at":"2025-05-22T16:01:49Z","title":"Contextual Learning for Stochastic Optimization"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":24},"total_outbound_references":30},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.16829."}