{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:FZIGVTB5YXYAJX55IESC26SL6Z","short_pith_number":"pith:FZIGVTB5","canonical_record":{"source":{"id":"2402.11973","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-19T09:19:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"470b93d7a983111a97e3908d783197053300c8b42e7b39142d15a95dbf68737a","abstract_canon_sha256":"007cf41d207f6d0836cae269dffc579ecfa11fb1715f7d6ad24ccb06567f1427"},"schema_version":"1.0"},"canonical_sha256":"2e506acc3dc5f004dfbd41242d7a4bf64820a2bf58c33414b298135d6b5bfbe7","source":{"kind":"arxiv","id":"2402.11973","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.11973","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"arxiv_version","alias_value":"2402.11973v1","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11973","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_12","alias_value":"FZIGVTB5YXYA","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_16","alias_value":"FZIGVTB5YXYAJX55","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_8","alias_value":"FZIGVTB5","created_at":"2026-07-05T07:46:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:FZIGVTB5YXYAJX55IESC26SL6Z","target":"record","payload":{"canonical_record":{"source":{"id":"2402.11973","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-19T09:19:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"470b93d7a983111a97e3908d783197053300c8b42e7b39142d15a95dbf68737a","abstract_canon_sha256":"007cf41d207f6d0836cae269dffc579ecfa11fb1715f7d6ad24ccb06567f1427"},"schema_version":"1.0"},"canonical_sha256":"2e506acc3dc5f004dfbd41242d7a4bf64820a2bf58c33414b298135d6b5bfbe7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:46.828937Z","signature_b64":"ewkuVHl4//W9TwC8jWGkzsu1ZoPh0Ee++LakiwCLscanqgRetEnn4gfFvbLb0IkO8veprZrLx0IM+Epmv/f2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e506acc3dc5f004dfbd41242d7a4bf64820a2bf58c33414b298135d6b5bfbe7","last_reissued_at":"2026-07-05T07:46:46.828462Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:46.828462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.11973","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":"2026-07-05T07:46:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pSogq3dH/Ftkr2BA/cO6drGn8a0UUprUjewS8FsnCK8RhDbXXFUduUEvl6OcMU5RN/VhQJE8VS42oLdvpt/9BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:31:48.110860Z"},"content_sha256":"f423d905c14f7114956e642924a6416f333e1a491f501156c3dddae09a1d3e38","schema_version":"1.0","event_id":"sha256:f423d905c14f7114956e642924a6416f333e1a491f501156c3dddae09a1d3e38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:FZIGVTB5YXYAJX55IESC26SL6Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Active Learning for Censored Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Christoffer Riis, Filipe Rodrigues, Francisco C\\^amara Pereira, Frederik Boe H\\\"uttel","submitted_at":"2024-02-19T09:19:01Z","abstract_excerpt":"Bayesian active learning is based on information theoretical approaches that focus on maximising the information that new observations provide to the model parameters. This is commonly done by maximising the Bayesian Active Learning by Disagreement (BALD) acquisitions function. However, we highlight that it is challenging to estimate BALD when the new data points are subject to censorship, where only clipped values of the targets are observed. To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11973","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/2402.11973/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":"2026-07-05T07:46:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qcP+rTtuY1LP5/WRpqBiL2Sz+sOtBOmvaXSylr9Bumbm6JOM79XQf055fCY1tapLAXZNagris1oLbWRQlNRuDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:31:48.111223Z"},"content_sha256":"254d05128fb9725e32a95d227cd2da9afb1563c400ade6f926bfec2ee930a03d","schema_version":"1.0","event_id":"sha256:254d05128fb9725e32a95d227cd2da9afb1563c400ade6f926bfec2ee930a03d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FZIGVTB5YXYAJX55IESC26SL6Z/bundle.json","state_url":"https://pith.science/pith/FZIGVTB5YXYAJX55IESC26SL6Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FZIGVTB5YXYAJX55IESC26SL6Z/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":"2026-07-06T19:31:48Z","links":{"resolver":"https://pith.science/pith/FZIGVTB5YXYAJX55IESC26SL6Z","bundle":"https://pith.science/pith/FZIGVTB5YXYAJX55IESC26SL6Z/bundle.json","state":"https://pith.science/pith/FZIGVTB5YXYAJX55IESC26SL6Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FZIGVTB5YXYAJX55IESC26SL6Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:FZIGVTB5YXYAJX55IESC26SL6Z","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":"007cf41d207f6d0836cae269dffc579ecfa11fb1715f7d6ad24ccb06567f1427","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-19T09:19:01Z","title_canon_sha256":"470b93d7a983111a97e3908d783197053300c8b42e7b39142d15a95dbf68737a"},"schema_version":"1.0","source":{"id":"2402.11973","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.11973","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"arxiv_version","alias_value":"2402.11973v1","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11973","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_12","alias_value":"FZIGVTB5YXYA","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_16","alias_value":"FZIGVTB5YXYAJX55","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_8","alias_value":"FZIGVTB5","created_at":"2026-07-05T07:46:46Z"}],"graph_snapshots":[{"event_id":"sha256:254d05128fb9725e32a95d227cd2da9afb1563c400ade6f926bfec2ee930a03d","target":"graph","created_at":"2026-07-05T07:46:46Z","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/2402.11973/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian active learning is based on information theoretical approaches that focus on maximising the information that new observations provide to the model parameters. This is commonly done by maximising the Bayesian Active Learning by Disagreement (BALD) acquisitions function. However, we highlight that it is challenging to estimate BALD when the new data points are subject to censorship, where only clipped values of the targets are observed. To address this, we derive the entropy and the mutual information for censored distributions and derive the BALD objective for active learning in censor","authors_text":"Christoffer Riis, Filipe Rodrigues, Francisco C\\^amara Pereira, Frederik Boe H\\\"uttel","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-19T09:19:01Z","title":"Bayesian Active Learning for Censored Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11973","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:f423d905c14f7114956e642924a6416f333e1a491f501156c3dddae09a1d3e38","target":"record","created_at":"2026-07-05T07:46:46Z","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":"007cf41d207f6d0836cae269dffc579ecfa11fb1715f7d6ad24ccb06567f1427","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-19T09:19:01Z","title_canon_sha256":"470b93d7a983111a97e3908d783197053300c8b42e7b39142d15a95dbf68737a"},"schema_version":"1.0","source":{"id":"2402.11973","kind":"arxiv","version":1}},"canonical_sha256":"2e506acc3dc5f004dfbd41242d7a4bf64820a2bf58c33414b298135d6b5bfbe7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e506acc3dc5f004dfbd41242d7a4bf64820a2bf58c33414b298135d6b5bfbe7","first_computed_at":"2026-07-05T07:46:46.828462Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:46:46.828462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ewkuVHl4//W9TwC8jWGkzsu1ZoPh0Ee++LakiwCLscanqgRetEnn4gfFvbLb0IkO8veprZrLx0IM+Epmv/f2BA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:46:46.828937Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.11973","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f423d905c14f7114956e642924a6416f333e1a491f501156c3dddae09a1d3e38","sha256:254d05128fb9725e32a95d227cd2da9afb1563c400ade6f926bfec2ee930a03d"],"state_sha256":"0c68ab0513bf167a015e8b5d487e2f339c8137d1caf95b7eed531b65df194539"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eNExGMPfYTM3PpJbhGLmtVy1xzeqldb5X6jGbzS0XQZY09T1UYB5zRDkGH1b6HIABy3q1dZQlWcuhuNhWnADBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:31:48.113059Z","bundle_sha256":"8970c5bf4ec3b34bdc5edaae9d0e3d2f4761dda927bb6d4157c2c13a0b6fd5ae"}}