{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:IAGSX6PH43BWLRHMGGFHOTA2SW","short_pith_number":"pith:IAGSX6PH","schema_version":"1.0","canonical_sha256":"400d2bf9e7e6c365c4ec318a774c1a9586defbb0a422defce00a28dda8a78577","source":{"kind":"arxiv","id":"1504.00091","version":2},"attestation_state":"computed","paper":{"title":"Learning in the Presence of Corruption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Brendan van Rooyen, Robert C. Williamson","submitted_at":"2015-04-01T02:54:38Z","abstract_excerpt":"In supervised learning one wishes to identify a pattern present in a joint distribution $P$, of instances, label pairs, by providing a function $f$ from instances to labels that has low risk $\\mathbb{E}_{P}\\ell(y,f(x))$. To do so, the learner is given access to $n$ iid samples drawn from $P$. In many real world problems clean samples are not available. Rather, the learner is given access to samples from a corrupted distribution $\\tilde{P}$ from which to learn, while the goal of predicting the clean pattern remains. There are many different types of corruption one can consider, and as of yet th"},"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":"1504.00091","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-04-01T02:54:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"82752614d2414dee783109112fdc1b48b68c0bfa12aacc84c4200e123b0ad96c","abstract_canon_sha256":"328de95b7455b9d82d3c6e84ff40b5ff0594833b6c3af2aead2ad306dffcfda8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:37:18.890209Z","signature_b64":"uEoBbUEQCuzI986kLJ+nlQCTRB/8x8OklFiiQulr8XzwHrjwpYkqBKyL1R7WmzP4WrRcFYpZMgfxoBYt3sgxCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"400d2bf9e7e6c365c4ec318a774c1a9586defbb0a422defce00a28dda8a78577","last_reissued_at":"2026-05-18T01:37:18.889473Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:37:18.889473Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning in the Presence of Corruption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Brendan van Rooyen, Robert C. Williamson","submitted_at":"2015-04-01T02:54:38Z","abstract_excerpt":"In supervised learning one wishes to identify a pattern present in a joint distribution $P$, of instances, label pairs, by providing a function $f$ from instances to labels that has low risk $\\mathbb{E}_{P}\\ell(y,f(x))$. To do so, the learner is given access to $n$ iid samples drawn from $P$. In many real world problems clean samples are not available. Rather, the learner is given access to samples from a corrupted distribution $\\tilde{P}$ from which to learn, while the goal of predicting the clean pattern remains. There are many different types of corruption one can consider, and as of yet th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.00091","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":""},"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":"1504.00091","created_at":"2026-05-18T01:37:18.889584+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.00091v2","created_at":"2026-05-18T01:37:18.889584+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.00091","created_at":"2026-05-18T01:37:18.889584+00:00"},{"alias_kind":"pith_short_12","alias_value":"IAGSX6PH43BW","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_16","alias_value":"IAGSX6PH43BWLRHM","created_at":"2026-05-18T12:29:25.134429+00:00"},{"alias_kind":"pith_short_8","alias_value":"IAGSX6PH","created_at":"2026-05-18T12:29:25.134429+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/IAGSX6PH43BWLRHMGGFHOTA2SW","json":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW.json","graph_json":"https://pith.science/api/pith-number/IAGSX6PH43BWLRHMGGFHOTA2SW/graph.json","events_json":"https://pith.science/api/pith-number/IAGSX6PH43BWLRHMGGFHOTA2SW/events.json","paper":"https://pith.science/paper/IAGSX6PH"},"agent_actions":{"view_html":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW","download_json":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW.json","view_paper":"https://pith.science/paper/IAGSX6PH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.00091&json=true","fetch_graph":"https://pith.science/api/pith-number/IAGSX6PH43BWLRHMGGFHOTA2SW/graph.json","fetch_events":"https://pith.science/api/pith-number/IAGSX6PH43BWLRHMGGFHOTA2SW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW/action/storage_attestation","attest_author":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW/action/author_attestation","sign_citation":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW/action/citation_signature","submit_replication":"https://pith.science/pith/IAGSX6PH43BWLRHMGGFHOTA2SW/action/replication_record"}},"created_at":"2026-05-18T01:37:18.889584+00:00","updated_at":"2026-05-18T01:37:18.889584+00:00"}