{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VK65K5MHEUO6ICMUVBEEO6GGXA","short_pith_number":"pith:VK65K5MH","schema_version":"1.0","canonical_sha256":"aabdd57587251de40994a8484778c6b805be0f7459c9fac379af815ba86174b3","source":{"kind":"arxiv","id":"2505.16126","version":2},"attestation_state":"computed","paper":{"title":"Robust Invariant Representation Learning by Distribution Extrapolation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Konstantinos Slavakis, Kotaro Yoshida","submitted_at":"2025-05-22T02:03:34Z","abstract_excerpt":"Invariant risk minimization (IRM) aims to enable out-of-distribution (OOD) generalization in deep learning by learning invariant representations. As IRM poses an inherently challenging bi-level optimization problem, most existing approaches -- including IRMv1 -- adopt penalty-based single-level approximations. However, empirical studies consistently show that these methods often fail to outperform well-tuned empirical risk minimization (ERM), highlighting the need for more robust IRM implementations. This work theoretically identifies a key limitation common to many IRM variants: their penalty"},"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":"2505.16126","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-22T02:03:34Z","cross_cats_sorted":[],"title_canon_sha256":"4af22b1ad236540b3319d8b674f0505bb524cea0fdb86561dce470a84d09fd9a","abstract_canon_sha256":"cb61b59f42b0a92e89993774f0d6bc6503ffb5f2e2342580819781204c7625cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:21.909722Z","signature_b64":"CiSXbwvEXusKEc9alBN5O/tIcfUqxI8SXuc+LSjwgBbeIsnwGL23ittM4zxWWdWrm+hQ/CSci4FPULnO6YrYBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aabdd57587251de40994a8484778c6b805be0f7459c9fac379af815ba86174b3","last_reissued_at":"2026-07-05T11:08:21.908555Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:21.908555Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Invariant Representation Learning by Distribution Extrapolation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Konstantinos Slavakis, Kotaro Yoshida","submitted_at":"2025-05-22T02:03:34Z","abstract_excerpt":"Invariant risk minimization (IRM) aims to enable out-of-distribution (OOD) generalization in deep learning by learning invariant representations. As IRM poses an inherently challenging bi-level optimization problem, most existing approaches -- including IRMv1 -- adopt penalty-based single-level approximations. However, empirical studies consistently show that these methods often fail to outperform well-tuned empirical risk minimization (ERM), highlighting the need for more robust IRM implementations. This work theoretically identifies a key limitation common to many IRM variants: their penalty"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16126","kind":"arxiv","version":2},"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/2505.16126/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":"2505.16126","created_at":"2026-07-05T11:08:21.908939+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.16126v2","created_at":"2026-07-05T11:08:21.908939+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16126","created_at":"2026-07-05T11:08:21.908939+00:00"},{"alias_kind":"pith_short_12","alias_value":"VK65K5MHEUO6","created_at":"2026-07-05T11:08:21.908939+00:00"},{"alias_kind":"pith_short_16","alias_value":"VK65K5MHEUO6ICMU","created_at":"2026-07-05T11:08:21.908939+00:00"},{"alias_kind":"pith_short_8","alias_value":"VK65K5MH","created_at":"2026-07-05T11:08:21.908939+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05025","citing_title":"Invariant Gradient Alignment for Robust Reasoning Distillation","ref_index":32,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA","json":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA.json","graph_json":"https://pith.science/api/pith-number/VK65K5MHEUO6ICMUVBEEO6GGXA/graph.json","events_json":"https://pith.science/api/pith-number/VK65K5MHEUO6ICMUVBEEO6GGXA/events.json","paper":"https://pith.science/paper/VK65K5MH"},"agent_actions":{"view_html":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA","download_json":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA.json","view_paper":"https://pith.science/paper/VK65K5MH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.16126&json=true","fetch_graph":"https://pith.science/api/pith-number/VK65K5MHEUO6ICMUVBEEO6GGXA/graph.json","fetch_events":"https://pith.science/api/pith-number/VK65K5MHEUO6ICMUVBEEO6GGXA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA/action/storage_attestation","attest_author":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA/action/author_attestation","sign_citation":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA/action/citation_signature","submit_replication":"https://pith.science/pith/VK65K5MHEUO6ICMUVBEEO6GGXA/action/replication_record"}},"created_at":"2026-07-05T11:08:21.908939+00:00","updated_at":"2026-07-05T11:08:21.908939+00:00"}