{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YNPFXVOC2NOPYORUXCBQYI5LNL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ce16fa000fa3f00949430ba818c25f5a2785031243c070276678560082cd53ab","cross_cats_sorted":["cs.AI","cs.LG","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-24T12:52:55Z","title_canon_sha256":"38147327cb83b38e472952611e793797758ebbf8e3fe31a1a510209026fe24f6"},"schema_version":"1.0","source":{"id":"2307.12754","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.12754","created_at":"2026-07-05T08:53:07Z"},{"alias_kind":"arxiv_version","alias_value":"2307.12754v4","created_at":"2026-07-05T08:53:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.12754","created_at":"2026-07-05T08:53:07Z"},{"alias_kind":"pith_short_12","alias_value":"YNPFXVOC2NOP","created_at":"2026-07-05T08:53:07Z"},{"alias_kind":"pith_short_16","alias_value":"YNPFXVOC2NOPYORU","created_at":"2026-07-05T08:53:07Z"},{"alias_kind":"pith_short_8","alias_value":"YNPFXVOC","created_at":"2026-07-05T08:53:07Z"}],"graph_snapshots":[{"event_id":"sha256:ccd30e2637947380e81df5be853cff06e4c3ccce16112e22c736b76d63405a65","target":"graph","created_at":"2026-07-05T08:53:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2307.12754/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Representation learning plays a crucial role in automated feature selection, particularly in the context of high-dimensional data, where non-parametric methods often struggle. In this study, we focus on supervised learning scenarios where the pertinent information resides within a lower-dimensional linear subspace of the data, namely the multi-index model. If this subspace were known, it would greatly enhance prediction, computation, and interpretation. To address this challenge, we propose a novel method for joint linear feature learning and non-parametric function estimation, aimed at more e","authors_text":"Bertille Follain, Francis Bach","cross_cats":["cs.AI","cs.LG","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-24T12:52:55Z","title":"Nonparametric Linear Feature Learning in Regression Through Regularisation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.12754","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9a68352e03bf29c836f1314fc5fb650a56f0eaab498251a82da4c374bf0f5cf3","target":"record","created_at":"2026-07-05T08:53:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ce16fa000fa3f00949430ba818c25f5a2785031243c070276678560082cd53ab","cross_cats_sorted":["cs.AI","cs.LG","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-07-24T12:52:55Z","title_canon_sha256":"38147327cb83b38e472952611e793797758ebbf8e3fe31a1a510209026fe24f6"},"schema_version":"1.0","source":{"id":"2307.12754","kind":"arxiv","version":4}},"canonical_sha256":"c35e5bd5c2d35cfc3a34b8830c23ab6ac6d2ec1d64f2a7ebb74fb8aa3bf34e40","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c35e5bd5c2d35cfc3a34b8830c23ab6ac6d2ec1d64f2a7ebb74fb8aa3bf34e40","first_computed_at":"2026-07-05T08:53:07.679701Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:53:07.679701Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QHH48SQX2lBZPnDEuQuvefAYqiDQHpcZ+LIZMMr1A0g9kXCfgPqJb6t9KcuURXSZdBXAF/4m42yA0z4zCO2UCA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:53:07.680172Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.12754","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a68352e03bf29c836f1314fc5fb650a56f0eaab498251a82da4c374bf0f5cf3","sha256:ccd30e2637947380e81df5be853cff06e4c3ccce16112e22c736b76d63405a65"],"state_sha256":"f705e4e2c61c98cfa7c372aeaed166adbfb69449c3e49606ee72ae9e1f91057e"}