{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OK3I2K65YNFNLJYXPKJO4JLPCU","short_pith_number":"pith:OK3I2K65","schema_version":"1.0","canonical_sha256":"72b68d2bddc34ad5a7177a92ee256f150a28537fbf3a8d5c988ff58f2132d616","source":{"kind":"arxiv","id":"2203.03597","version":2},"attestation_state":"computed","paper":{"title":"Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Fanny Yang, Konstantin Donhauser, Nicolo Ruggeri, Stefan Stojanovic","submitted_at":"2022-03-07T18:44:47Z","abstract_excerpt":"Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even though this intuition is valid for regularized models, in this paper we caution against a strong inductive bias for interpolation in the presence of noise: While a stronger inductive bias encourages a simpler structure that is more aligned with the ground truth, it also increases the detrimental effect of noise. Specifically, for both linear regression and classification with a sparse ground truth, we prove that min"},"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":"2203.03597","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-07T18:44:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0de80294dc98234ad3740fef7f6175423ddd312eec335307d5a14eca54110074","abstract_canon_sha256":"17b97421c5af9dc0806a73cb3a858571929902d4e7bd09379ba3d9ddfcacf89c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:10:54.586729Z","signature_b64":"bCuBNJ7R2MDeJL6tSh8pxs6ZtjM6ZtvkXBNjjSD8mhJmNVRC6v83LVA+C9FDJTRb2Dmr+3XJ/Sg8dVuZNp1VBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72b68d2bddc34ad5a7177a92ee256f150a28537fbf3a8d5c988ff58f2132d616","last_reissued_at":"2026-07-05T05:10:54.586199Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:10:54.586199Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Fanny Yang, Konstantin Donhauser, Nicolo Ruggeri, Stefan Stojanovic","submitted_at":"2022-03-07T18:44:47Z","abstract_excerpt":"Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even though this intuition is valid for regularized models, in this paper we caution against a strong inductive bias for interpolation in the presence of noise: While a stronger inductive bias encourages a simpler structure that is more aligned with the ground truth, it also increases the detrimental effect of noise. Specifically, for both linear regression and classification with a sparse ground truth, we prove that min"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.03597","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/2203.03597/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":"2203.03597","created_at":"2026-07-05T05:10:54.586267+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.03597v2","created_at":"2026-07-05T05:10:54.586267+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.03597","created_at":"2026-07-05T05:10:54.586267+00:00"},{"alias_kind":"pith_short_12","alias_value":"OK3I2K65YNFN","created_at":"2026-07-05T05:10:54.586267+00:00"},{"alias_kind":"pith_short_16","alias_value":"OK3I2K65YNFNLJYX","created_at":"2026-07-05T05:10:54.586267+00:00"},{"alias_kind":"pith_short_8","alias_value":"OK3I2K65","created_at":"2026-07-05T05:10:54.586267+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07694","citing_title":"Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity","ref_index":139,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU","json":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU.json","graph_json":"https://pith.science/api/pith-number/OK3I2K65YNFNLJYXPKJO4JLPCU/graph.json","events_json":"https://pith.science/api/pith-number/OK3I2K65YNFNLJYXPKJO4JLPCU/events.json","paper":"https://pith.science/paper/OK3I2K65"},"agent_actions":{"view_html":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU","download_json":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU.json","view_paper":"https://pith.science/paper/OK3I2K65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.03597&json=true","fetch_graph":"https://pith.science/api/pith-number/OK3I2K65YNFNLJYXPKJO4JLPCU/graph.json","fetch_events":"https://pith.science/api/pith-number/OK3I2K65YNFNLJYXPKJO4JLPCU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU/action/storage_attestation","attest_author":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU/action/author_attestation","sign_citation":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU/action/citation_signature","submit_replication":"https://pith.science/pith/OK3I2K65YNFNLJYXPKJO4JLPCU/action/replication_record"}},"created_at":"2026-07-05T05:10:54.586267+00:00","updated_at":"2026-07-05T05:10:54.586267+00:00"}