{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:OC6FGVJ7LZFGROFJUYPIDPWPW4","short_pith_number":"pith:OC6FGVJ7","canonical_record":{"source":{"id":"2203.07512","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-03-14T21:42:21Z","cross_cats_sorted":["cs.AI","cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"1288a869eb9f4cde024e2e2a1c6cb08deea73d803c586fa6b6abbc8915eed746","abstract_canon_sha256":"a86608b819bf336115b85cb2686248c63923a3565c26eea7914062b44ea7da0f"},"schema_version":"1.0"},"canonical_sha256":"70bc53553f5e4a68b8a9a61e81becfb73032542ca15162672fce802a7ee73eac","source":{"kind":"arxiv","id":"2203.07512","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.07512","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"arxiv_version","alias_value":"2203.07512v3","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.07512","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"pith_short_12","alias_value":"OC6FGVJ7LZFG","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"pith_short_16","alias_value":"OC6FGVJ7LZFGROFJ","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"pith_short_8","alias_value":"OC6FGVJ7","created_at":"2026-07-05T05:47:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:OC6FGVJ7LZFGROFJUYPIDPWPW4","target":"record","payload":{"canonical_record":{"source":{"id":"2203.07512","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-03-14T21:42:21Z","cross_cats_sorted":["cs.AI","cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"1288a869eb9f4cde024e2e2a1c6cb08deea73d803c586fa6b6abbc8915eed746","abstract_canon_sha256":"a86608b819bf336115b85cb2686248c63923a3565c26eea7914062b44ea7da0f"},"schema_version":"1.0"},"canonical_sha256":"70bc53553f5e4a68b8a9a61e81becfb73032542ca15162672fce802a7ee73eac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:44.557639Z","signature_b64":"EsFU1KW8gnmWfl/J8rleE9gwl7HEMdv8Jsi8U+P9VydgWLpxRAtQpIRi8m1xXY7JUHpYdKBQN11A59FhyIQnAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70bc53553f5e4a68b8a9a61e81becfb73032542ca15162672fce802a7ee73eac","last_reissued_at":"2026-07-05T05:47:44.557168Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:44.557168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.07512","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:47:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FiPqRTYFLomLLUySUgu0sLKonBKN32eTtyvGl5bpIYDgKDUdF3qGdDLko2vn8HI05ynJZt2RVfv/KFB9FIv+BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:37:40.584303Z"},"content_sha256":"2720a72f0d946644bbd0fed88f91a8be5e7a000f7615fe70567b0c72fc419d5d","schema_version":"1.0","event_id":"sha256:2720a72f0d946644bbd0fed88f91a8be5e7a000f7615fe70567b0c72fc419d5d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:OC6FGVJ7LZFGROFJUYPIDPWPW4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Don't fear the unlabelled: safe semi-supervised learning via simple debiasing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei","submitted_at":"2022-03-14T21:42:21Z","abstract_excerpt":"Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount of attention in the past years, most methods present the common drawback of lacking theoretical guarantees. Our starting point is to notice that the estimate of the risk that most discriminative SSL methods minimise is biased, even asymptotically. This bias impedes the use of standard statistical learning theory and can hurt empirical performance. We propose a simple way of removing the bias. Our debiasing approach is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.07512","kind":"arxiv","version":3},"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/2203.07512/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":"2026-07-05T05:47:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pa1amBpLTpJw6++KfBln7r2dq6uQOmoHKGLRSJ+u6tmyDyriy1MtbAenM7D5QXXWHSkh3MqAtFNc/OhYYX6CAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T08:37:40.584717Z"},"content_sha256":"3c35afdf4441859f88402a1cf6ad7c2740d67baa88e894709b6e482731046413","schema_version":"1.0","event_id":"sha256:3c35afdf4441859f88402a1cf6ad7c2740d67baa88e894709b6e482731046413"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OC6FGVJ7LZFGROFJUYPIDPWPW4/bundle.json","state_url":"https://pith.science/pith/OC6FGVJ7LZFGROFJUYPIDPWPW4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OC6FGVJ7LZFGROFJUYPIDPWPW4/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":"2026-07-07T08:37:40Z","links":{"resolver":"https://pith.science/pith/OC6FGVJ7LZFGROFJUYPIDPWPW4","bundle":"https://pith.science/pith/OC6FGVJ7LZFGROFJUYPIDPWPW4/bundle.json","state":"https://pith.science/pith/OC6FGVJ7LZFGROFJUYPIDPWPW4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OC6FGVJ7LZFGROFJUYPIDPWPW4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:OC6FGVJ7LZFGROFJUYPIDPWPW4","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":"a86608b819bf336115b85cb2686248c63923a3565c26eea7914062b44ea7da0f","cross_cats_sorted":["cs.AI","cs.LG","stat.CO","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-03-14T21:42:21Z","title_canon_sha256":"1288a869eb9f4cde024e2e2a1c6cb08deea73d803c586fa6b6abbc8915eed746"},"schema_version":"1.0","source":{"id":"2203.07512","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.07512","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"arxiv_version","alias_value":"2203.07512v3","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.07512","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"pith_short_12","alias_value":"OC6FGVJ7LZFG","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"pith_short_16","alias_value":"OC6FGVJ7LZFGROFJ","created_at":"2026-07-05T05:47:44Z"},{"alias_kind":"pith_short_8","alias_value":"OC6FGVJ7","created_at":"2026-07-05T05:47:44Z"}],"graph_snapshots":[{"event_id":"sha256:3c35afdf4441859f88402a1cf6ad7c2740d67baa88e894709b6e482731046413","target":"graph","created_at":"2026-07-05T05:47:44Z","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/2203.07512/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount of attention in the past years, most methods present the common drawback of lacking theoretical guarantees. Our starting point is to notice that the estimate of the risk that most discriminative SSL methods minimise is biased, even asymptotically. This bias impedes the use of standard statistical learning theory and can hurt empirical performance. We propose a simple way of removing the bias. Our debiasing approach is","authors_text":"Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei","cross_cats":["cs.AI","cs.LG","stat.CO","stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-03-14T21:42:21Z","title":"Don't fear the unlabelled: safe semi-supervised learning via simple debiasing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.07512","kind":"arxiv","version":3},"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":"sha256:2720a72f0d946644bbd0fed88f91a8be5e7a000f7615fe70567b0c72fc419d5d","target":"record","created_at":"2026-07-05T05:47:44Z","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":"a86608b819bf336115b85cb2686248c63923a3565c26eea7914062b44ea7da0f","cross_cats_sorted":["cs.AI","cs.LG","stat.CO","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2022-03-14T21:42:21Z","title_canon_sha256":"1288a869eb9f4cde024e2e2a1c6cb08deea73d803c586fa6b6abbc8915eed746"},"schema_version":"1.0","source":{"id":"2203.07512","kind":"arxiv","version":3}},"canonical_sha256":"70bc53553f5e4a68b8a9a61e81becfb73032542ca15162672fce802a7ee73eac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"70bc53553f5e4a68b8a9a61e81becfb73032542ca15162672fce802a7ee73eac","first_computed_at":"2026-07-05T05:47:44.557168Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:47:44.557168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EsFU1KW8gnmWfl/J8rleE9gwl7HEMdv8Jsi8U+P9VydgWLpxRAtQpIRi8m1xXY7JUHpYdKBQN11A59FhyIQnAg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:47:44.557639Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.07512","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2720a72f0d946644bbd0fed88f91a8be5e7a000f7615fe70567b0c72fc419d5d","sha256:3c35afdf4441859f88402a1cf6ad7c2740d67baa88e894709b6e482731046413"],"state_sha256":"0a20d51a826ac6e9e8eca8af20141d219a78bb3b5183f7c3863475d03a3780ce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QlOBZcqbpluMOCcEWQJ03B9Ky0t1XiEdZGNRv/5cSCcJMSUFBaqC2rS6LiKc2jn2ALi7Fhm1Za8FosaBBmQLBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T08:37:40.586686Z","bundle_sha256":"07c33ddfe22f3db78b6fefbc1b46d311d693c761485aa55c94bce365d96d7d87"}}