{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KM74LV64PLKXIRRS5N7URC52LT","short_pith_number":"pith:KM74LV64","schema_version":"1.0","canonical_sha256":"533fc5d7dc7ad5744632eb7f488bba5cd915e5c48f70c0bfb6d08236dbfa4025","source":{"kind":"arxiv","id":"2407.04370","version":2},"attestation_state":"computed","paper":{"title":"Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ajmal Mian, Mubarak Shah, Naveed Akhtar, Peiyu Yang","submitted_at":"2024-07-05T09:16:56Z","abstract_excerpt":"Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input. Within our approach, robust feature attributions exhibit a certain consistency, while non-robust feature attributions are susceptible to fluctuations. This behavior allows identification of correlation between model reliance on non-robust features and smoothness of marginal density of the input samples. Hence, we uniquely regularize the gradients of the marginal density w.r.t. the "},"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":"2407.04370","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-07-05T09:16:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f4112817bf4ba831c6cb01711cd6cbfed83e126a5ff77932c67eb83249f7bd2d","abstract_canon_sha256":"9df78ad9a857a36064f3a4c86f25675568ce629b0c2f65029a87e5e911a31b4a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:46.893194Z","signature_b64":"bvxMKpV1A/1RyeH3/f0TvPZexvtUwcCQy/Bedt99tAJMhh9ycvB4lnx3ZRuc3H2t+opEngPOrAQgDRqv4hrKBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"533fc5d7dc7ad5744632eb7f488bba5cd915e5c48f70c0bfb6d08236dbfa4025","last_reissued_at":"2026-07-05T08:41:46.892663Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:46.892663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ajmal Mian, Mubarak Shah, Naveed Akhtar, Peiyu Yang","submitted_at":"2024-07-05T09:16:56Z","abstract_excerpt":"Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attributing model predictions to the input. Within our approach, robust feature attributions exhibit a certain consistency, while non-robust feature attributions are susceptible to fluctuations. This behavior allows identification of correlation between model reliance on non-robust features and smoothness of marginal density of the input samples. Hence, we uniquely regularize the gradients of the marginal density w.r.t. the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.04370","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/2407.04370/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":"2407.04370","created_at":"2026-07-05T08:41:46.892727+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.04370v2","created_at":"2026-07-05T08:41:46.892727+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.04370","created_at":"2026-07-05T08:41:46.892727+00:00"},{"alias_kind":"pith_short_12","alias_value":"KM74LV64PLKX","created_at":"2026-07-05T08:41:46.892727+00:00"},{"alias_kind":"pith_short_16","alias_value":"KM74LV64PLKXIRRS","created_at":"2026-07-05T08:41:46.892727+00:00"},{"alias_kind":"pith_short_8","alias_value":"KM74LV64","created_at":"2026-07-05T08:41:46.892727+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/KM74LV64PLKXIRRS5N7URC52LT","json":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT.json","graph_json":"https://pith.science/api/pith-number/KM74LV64PLKXIRRS5N7URC52LT/graph.json","events_json":"https://pith.science/api/pith-number/KM74LV64PLKXIRRS5N7URC52LT/events.json","paper":"https://pith.science/paper/KM74LV64"},"agent_actions":{"view_html":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT","download_json":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT.json","view_paper":"https://pith.science/paper/KM74LV64","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.04370&json=true","fetch_graph":"https://pith.science/api/pith-number/KM74LV64PLKXIRRS5N7URC52LT/graph.json","fetch_events":"https://pith.science/api/pith-number/KM74LV64PLKXIRRS5N7URC52LT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT/action/storage_attestation","attest_author":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT/action/author_attestation","sign_citation":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT/action/citation_signature","submit_replication":"https://pith.science/pith/KM74LV64PLKXIRRS5N7URC52LT/action/replication_record"}},"created_at":"2026-07-05T08:41:46.892727+00:00","updated_at":"2026-07-05T08:41:46.892727+00:00"}