{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NVBOY5737ZXU5NDNBAUP2CYIBV","short_pith_number":"pith:NVBOY573","schema_version":"1.0","canonical_sha256":"6d42ec77fbfe6f4eb46d0828fd0b080d5b8c09ad82110c55f6d9480154c6d0c3","source":{"kind":"arxiv","id":"2508.19486","version":1},"attestation_state":"computed","paper":{"title":"Distribution Shift Aware Neural Tabular Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arun Vignesh Malarkkan, Chandan K. Reddy, Dongjie Wang, Nanxu Gong, Vivek Gupta, Wangyang Ying, Xinyuan Wang, Yanjie Fu","submitted_at":"2025-08-27T00:14:08Z","abstract_excerpt":"Tabular learning transforms raw features into optimized spaces for downstream tasks, but its effectiveness deteriorates under distribution shifts between training and testing data. We formalize this challenge as the Distribution Shift Tabular Learning (DSTL) problem and propose a novel Shift-Aware Feature Transformation (SAFT) framework to address it. SAFT reframes tabular learning from a discrete search task into a continuous representation-generation paradigm, enabling differentiable optimization over transformed feature sets. SAFT integrates three mechanisms to ensure robustness: (i) shift-"},"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":"2508.19486","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-27T00:14:08Z","cross_cats_sorted":[],"title_canon_sha256":"41d3eec6a2305e2c7a4a074756a6fe88d19517b73ca5f182a6f9aeef82f741aa","abstract_canon_sha256":"e5a803f4b18f07b989be984b1d0edb19acd6af77e3cdc18d009b493fe78c8a91"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:12.420142Z","signature_b64":"5pbaqJujjqG50ZCRTDLvdfoG/FaEOkbbVmI94EloGmuaIwGyLfdx5eJa9GdnDnTQ6VIEB3XMUkF2YebTLhN6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d42ec77fbfe6f4eb46d0828fd0b080d5b8c09ad82110c55f6d9480154c6d0c3","last_reissued_at":"2026-07-05T12:00:12.419723Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:12.419723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distribution Shift Aware Neural Tabular Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Arun Vignesh Malarkkan, Chandan K. Reddy, Dongjie Wang, Nanxu Gong, Vivek Gupta, Wangyang Ying, Xinyuan Wang, Yanjie Fu","submitted_at":"2025-08-27T00:14:08Z","abstract_excerpt":"Tabular learning transforms raw features into optimized spaces for downstream tasks, but its effectiveness deteriorates under distribution shifts between training and testing data. We formalize this challenge as the Distribution Shift Tabular Learning (DSTL) problem and propose a novel Shift-Aware Feature Transformation (SAFT) framework to address it. SAFT reframes tabular learning from a discrete search task into a continuous representation-generation paradigm, enabling differentiable optimization over transformed feature sets. SAFT integrates three mechanisms to ensure robustness: (i) shift-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.19486","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/2508.19486/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":"2508.19486","created_at":"2026-07-05T12:00:12.419788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.19486v1","created_at":"2026-07-05T12:00:12.419788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.19486","created_at":"2026-07-05T12:00:12.419788+00:00"},{"alias_kind":"pith_short_12","alias_value":"NVBOY5737ZXU","created_at":"2026-07-05T12:00:12.419788+00:00"},{"alias_kind":"pith_short_16","alias_value":"NVBOY5737ZXU5NDN","created_at":"2026-07-05T12:00:12.419788+00:00"},{"alias_kind":"pith_short_8","alias_value":"NVBOY573","created_at":"2026-07-05T12:00:12.419788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.00693","citing_title":"DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV","json":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV.json","graph_json":"https://pith.science/api/pith-number/NVBOY5737ZXU5NDNBAUP2CYIBV/graph.json","events_json":"https://pith.science/api/pith-number/NVBOY5737ZXU5NDNBAUP2CYIBV/events.json","paper":"https://pith.science/paper/NVBOY573"},"agent_actions":{"view_html":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV","download_json":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV.json","view_paper":"https://pith.science/paper/NVBOY573","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.19486&json=true","fetch_graph":"https://pith.science/api/pith-number/NVBOY5737ZXU5NDNBAUP2CYIBV/graph.json","fetch_events":"https://pith.science/api/pith-number/NVBOY5737ZXU5NDNBAUP2CYIBV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV/action/storage_attestation","attest_author":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV/action/author_attestation","sign_citation":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV/action/citation_signature","submit_replication":"https://pith.science/pith/NVBOY5737ZXU5NDNBAUP2CYIBV/action/replication_record"}},"created_at":"2026-07-05T12:00:12.419788+00:00","updated_at":"2026-07-05T12:00:12.419788+00:00"}