{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:TNHMPHZKYCAQD27BXTQ5YPXB6U","short_pith_number":"pith:TNHMPHZK","schema_version":"1.0","canonical_sha256":"9b4ec79f2ac08101ebe1bce1dc3ee1f52783fb754935bc8100bedd0254ed818b","source":{"kind":"arxiv","id":"2012.12155","version":1},"attestation_state":"computed","paper":{"title":"Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Gael Lederrey, Michel Bierlaire, Tim Hillel, Virginie Lurkin","submitted_at":"2020-12-22T16:43:29Z","abstract_excerpt":"The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the estimation of statistical Discrete Choice Models based on the random utility framework. In this article, we provide new ways of dealing with large datasets in the context of Discrete Choice Models. We achieve this by proposing new efficient stochastic optimization algorithms and extensively testing them alongside existing approaches. We develop t"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2012.12155","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2020-12-22T16:43:29Z","cross_cats_sorted":[],"title_canon_sha256":"7d6c7a86fdbb64943fe4fa307b4d6a2b11b68e48b2f30b36572f724e93308195","abstract_canon_sha256":"29f024ffd662c3f0ffb27269d47cb1fad1e44a8ae177f20276f4a80dcf2dfaf7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:01:25.270182Z","signature_b64":"AVgK201oawA77QW0BD7phJ9L3g9QXxfLlKecNn2N9BFy29rXZuG28ySPPUx/g4earBVFz6UQsB5vEumeFNnWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b4ec79f2ac08101ebe1bce1dc3ee1f52783fb754935bc8100bedd0254ed818b","last_reissued_at":"2026-07-05T02:01:25.269842Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:01:25.269842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Gael Lederrey, Michel Bierlaire, Tim Hillel, Virginie Lurkin","submitted_at":"2020-12-22T16:43:29Z","abstract_excerpt":"The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the estimation of statistical Discrete Choice Models based on the random utility framework. In this article, we provide new ways of dealing with large datasets in the context of Discrete Choice Models. We achieve this by proposing new efficient stochastic optimization algorithms and extensively testing them alongside existing approaches. We develop t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.12155","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2012.12155/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2012.12155","created_at":"2026-07-05T02:01:25.269897+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.12155v1","created_at":"2026-07-05T02:01:25.269897+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.12155","created_at":"2026-07-05T02:01:25.269897+00:00"},{"alias_kind":"pith_short_12","alias_value":"TNHMPHZKYCAQ","created_at":"2026-07-05T02:01:25.269897+00:00"},{"alias_kind":"pith_short_16","alias_value":"TNHMPHZKYCAQD27B","created_at":"2026-07-05T02:01:25.269897+00:00"},{"alias_kind":"pith_short_8","alias_value":"TNHMPHZK","created_at":"2026-07-05T02:01:25.269897+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U","json":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U.json","graph_json":"https://pith.science/api/pith-number/TNHMPHZKYCAQD27BXTQ5YPXB6U/graph.json","events_json":"https://pith.science/api/pith-number/TNHMPHZKYCAQD27BXTQ5YPXB6U/events.json","paper":"https://pith.science/paper/TNHMPHZK"},"agent_actions":{"view_html":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U","download_json":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U.json","view_paper":"https://pith.science/paper/TNHMPHZK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.12155&json=true","fetch_graph":"https://pith.science/api/pith-number/TNHMPHZKYCAQD27BXTQ5YPXB6U/graph.json","fetch_events":"https://pith.science/api/pith-number/TNHMPHZKYCAQD27BXTQ5YPXB6U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U/action/storage_attestation","attest_author":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U/action/author_attestation","sign_citation":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U/action/citation_signature","submit_replication":"https://pith.science/pith/TNHMPHZKYCAQD27BXTQ5YPXB6U/action/replication_record"}},"created_at":"2026-07-05T02:01:25.269897+00:00","updated_at":"2026-07-05T02:01:25.269897+00:00"}