{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:LFYU6V7FHNAQTFKMO5FJT73N3E","short_pith_number":"pith:LFYU6V7F","canonical_record":{"source":{"id":"2002.06541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-16T09:12:27Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"6e31674349f91bb05875ab023f6e13473de34078c448ce62389f21cb0a9fd568","abstract_canon_sha256":"328b2e272ca68635d781915ec1bd35964d5155d5f08404fd138f452c227da3aa"},"schema_version":"1.0"},"canonical_sha256":"59714f57e53b4109954c774a99ff6dd9091cea6269ba7baa2e6d33938d1ea16b","source":{"kind":"arxiv","id":"2002.06541","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.06541","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"arxiv_version","alias_value":"2002.06541v1","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06541","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_12","alias_value":"LFYU6V7FHNAQ","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_16","alias_value":"LFYU6V7FHNAQTFKM","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_8","alias_value":"LFYU6V7F","created_at":"2026-07-05T00:41:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:LFYU6V7FHNAQTFKMO5FJT73N3E","target":"record","payload":{"canonical_record":{"source":{"id":"2002.06541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-16T09:12:27Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"6e31674349f91bb05875ab023f6e13473de34078c448ce62389f21cb0a9fd568","abstract_canon_sha256":"328b2e272ca68635d781915ec1bd35964d5155d5f08404fd138f452c227da3aa"},"schema_version":"1.0"},"canonical_sha256":"59714f57e53b4109954c774a99ff6dd9091cea6269ba7baa2e6d33938d1ea16b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:41:07.209344Z","signature_b64":"mr4CCYC5mglDWveb9rQJq4UVc/DwNtgNLPeQgZ9IQnhTojKvhUZvkUCdbnKtdEFsv/0aM3Dh2BUUaB2VU0UfCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59714f57e53b4109954c774a99ff6dd9091cea6269ba7baa2e6d33938d1ea16b","last_reissued_at":"2026-07-05T00:41:07.208977Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:41:07.208977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.06541","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:41:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bxlITnciAQ/lrlSNm4CBq3AXKd01Y0eq3xVBXU+NhqMLdEQhYKHzdTncSuNZq+TJYRcLR2M83GwvkhGG6W/lDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T04:48:45.070500Z"},"content_sha256":"e7356781c0a1cfd332aa12de6f09c20b68ce1ac35a2387737168bfb1a0bae6fa","schema_version":"1.0","event_id":"sha256:e7356781c0a1cfd332aa12de6f09c20b68ce1ac35a2387737168bfb1a0bae6fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:LFYU6V7FHNAQTFKMO5FJT73N3E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Not to Learn in the Presence of Noisy Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Blair Chen, Liu Ziyin, Louis-Philippe Morency, Masahito Ueda, Paul Pu Liang, Ruslan Salakhutdinov, Ru Wang","submitted_at":"2020-02-16T09:12:27Z","abstract_excerpt":"Learning in the presence of label noise is a challenging yet important task: it is crucial to design models that are robust in the presence of mislabeled datasets. In this paper, we discover that a new class of loss functions called the gambler's loss provides strong robustness to label noise across various levels of corruption. We show that training with this loss function encourages the model to \"abstain\" from learning on the data points with noisy labels, resulting in a simple and effective method to improve robustness and generalization. In addition, we propose two practical extensions of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06541","kind":"arxiv","version":1},"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/2002.06541/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:41:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FIFpKad2yjpgiullo4oo1SxPvcrl7U3PaywoMY4fqLVmSTKvRhsJy+OghjWkDwrFNewDL1TI6+grY7CYHNZDCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T04:48:45.071573Z"},"content_sha256":"da09b94fce0f99a8e537a592802a939ea599efce1e55dc70f97549d6d26d25d7","schema_version":"1.0","event_id":"sha256:da09b94fce0f99a8e537a592802a939ea599efce1e55dc70f97549d6d26d25d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LFYU6V7FHNAQTFKMO5FJT73N3E/bundle.json","state_url":"https://pith.science/pith/LFYU6V7FHNAQTFKMO5FJT73N3E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LFYU6V7FHNAQTFKMO5FJT73N3E/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T04:48:45Z","links":{"resolver":"https://pith.science/pith/LFYU6V7FHNAQTFKMO5FJT73N3E","bundle":"https://pith.science/pith/LFYU6V7FHNAQTFKMO5FJT73N3E/bundle.json","state":"https://pith.science/pith/LFYU6V7FHNAQTFKMO5FJT73N3E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LFYU6V7FHNAQTFKMO5FJT73N3E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:LFYU6V7FHNAQTFKMO5FJT73N3E","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":"328b2e272ca68635d781915ec1bd35964d5155d5f08404fd138f452c227da3aa","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-16T09:12:27Z","title_canon_sha256":"6e31674349f91bb05875ab023f6e13473de34078c448ce62389f21cb0a9fd568"},"schema_version":"1.0","source":{"id":"2002.06541","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.06541","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"arxiv_version","alias_value":"2002.06541v1","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.06541","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_12","alias_value":"LFYU6V7FHNAQ","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_16","alias_value":"LFYU6V7FHNAQTFKM","created_at":"2026-07-05T00:41:07Z"},{"alias_kind":"pith_short_8","alias_value":"LFYU6V7F","created_at":"2026-07-05T00:41:07Z"}],"graph_snapshots":[{"event_id":"sha256:da09b94fce0f99a8e537a592802a939ea599efce1e55dc70f97549d6d26d25d7","target":"graph","created_at":"2026-07-05T00:41:07Z","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/2002.06541/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning in the presence of label noise is a challenging yet important task: it is crucial to design models that are robust in the presence of mislabeled datasets. In this paper, we discover that a new class of loss functions called the gambler's loss provides strong robustness to label noise across various levels of corruption. We show that training with this loss function encourages the model to \"abstain\" from learning on the data points with noisy labels, resulting in a simple and effective method to improve robustness and generalization. In addition, we propose two practical extensions of ","authors_text":"Blair Chen, Liu Ziyin, Louis-Philippe Morency, Masahito Ueda, Paul Pu Liang, Ruslan Salakhutdinov, Ru Wang","cross_cats":["cs.IT","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-16T09:12:27Z","title":"Learning Not to Learn in the Presence of Noisy Labels"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.06541","kind":"arxiv","version":1},"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:e7356781c0a1cfd332aa12de6f09c20b68ce1ac35a2387737168bfb1a0bae6fa","target":"record","created_at":"2026-07-05T00:41:07Z","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":"328b2e272ca68635d781915ec1bd35964d5155d5f08404fd138f452c227da3aa","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-16T09:12:27Z","title_canon_sha256":"6e31674349f91bb05875ab023f6e13473de34078c448ce62389f21cb0a9fd568"},"schema_version":"1.0","source":{"id":"2002.06541","kind":"arxiv","version":1}},"canonical_sha256":"59714f57e53b4109954c774a99ff6dd9091cea6269ba7baa2e6d33938d1ea16b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59714f57e53b4109954c774a99ff6dd9091cea6269ba7baa2e6d33938d1ea16b","first_computed_at":"2026-07-05T00:41:07.208977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:41:07.208977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mr4CCYC5mglDWveb9rQJq4UVc/DwNtgNLPeQgZ9IQnhTojKvhUZvkUCdbnKtdEFsv/0aM3Dh2BUUaB2VU0UfCg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:41:07.209344Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.06541","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e7356781c0a1cfd332aa12de6f09c20b68ce1ac35a2387737168bfb1a0bae6fa","sha256:da09b94fce0f99a8e537a592802a939ea599efce1e55dc70f97549d6d26d25d7"],"state_sha256":"616b28028588dd3161138b3511eb676117e69b3ce3c4e2d966d0fca28de36a00"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SkBqpOrQ7PgNd75gcoOTTdi7jmo4w6T7FW+biKDzeWjjdOII1gbBaK9r6B26+BRd26uwUlvznKfBTXH/wtSwAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T04:48:45.082928Z","bundle_sha256":"75d9e49c1fced9d2c4b10d93119062929ca2c5bbea3676c551d9a9403cf006f2"}}