{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:EOFE776AEVE7GREU5Z6BIYMBAX","short_pith_number":"pith:EOFE776A","canonical_record":{"source":{"id":"2208.00549","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T00:36:57Z","cross_cats_sorted":["cs.AI","cs.IR","cs.IT","math.IT"],"title_canon_sha256":"736f1bdb2408f797c4aecd16c5af5d9f8d8732bdd6d84e3b55e171b5abecf31d","abstract_canon_sha256":"9f8bf6bec6656ecb4c6d32e3dc44890a02bc7b933ac9b14f2bcd5dc6d9fa7d9a"},"schema_version":"1.0"},"canonical_sha256":"238a4fffc02549f34494ee7c14618105d1c30e8b3226c725a1806566f12d152d","source":{"kind":"arxiv","id":"2208.00549","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.00549","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"arxiv_version","alias_value":"2208.00549v2","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00549","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"pith_short_12","alias_value":"EOFE776AEVE7","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"pith_short_16","alias_value":"EOFE776AEVE7GREU","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"pith_short_8","alias_value":"EOFE776A","created_at":"2026-07-05T05:13:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:EOFE776AEVE7GREU5Z6BIYMBAX","target":"record","payload":{"canonical_record":{"source":{"id":"2208.00549","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T00:36:57Z","cross_cats_sorted":["cs.AI","cs.IR","cs.IT","math.IT"],"title_canon_sha256":"736f1bdb2408f797c4aecd16c5af5d9f8d8732bdd6d84e3b55e171b5abecf31d","abstract_canon_sha256":"9f8bf6bec6656ecb4c6d32e3dc44890a02bc7b933ac9b14f2bcd5dc6d9fa7d9a"},"schema_version":"1.0"},"canonical_sha256":"238a4fffc02549f34494ee7c14618105d1c30e8b3226c725a1806566f12d152d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:36.762767Z","signature_b64":"8FSqyg5F3O4zAk8jwBkBcfOOZaM0BIR0DXEknadp5mkl3Ekq9DzL5G/VgxTZpLLf2FAv/6VnJsbhbHRGwphSAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"238a4fffc02549f34494ee7c14618105d1c30e8b3226c725a1806566f12d152d","last_reissued_at":"2026-07-05T05:13:36.762289Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:36.762289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.00549","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:13:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EBKdTlsAhib4ZMO276wbtaGIDTCEQ7HWgD8plaX7B2hFELhWuFRRXkfBOVHr62pGUXk//MIrkmaC6TdbKrDOBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T23:16:03.072252Z"},"content_sha256":"1abf53f00d5895c8178dee851f655ac5c50d4a39669a363a005be3ecc8a18ca8","schema_version":"1.0","event_id":"sha256:1abf53f00d5895c8178dee851f655ac5c50d4a39669a363a005be3ecc8a18ca8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:EOFE776AEVE7GREU5Z6BIYMBAX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.IT","math.IT"],"primary_cat":"cs.LG","authors_text":"Andreas Kirsch, Yarin Gal","submitted_at":"2022-08-01T00:36:57Z","abstract_excerpt":"Recently proposed methods in data subset selection, that is active learning and active sampling, use Fisher information, Hessians, similarity matrices based on gradients, and gradient lengths to estimate how informative data is for a model's training. Are these different approaches connected, and if so, how? We revisit the fundamentals of Bayesian optimal experiment design and show that these recently proposed methods can be understood as approximations to information-theoretic quantities: among them, the mutual information between predictions and model parameters, known as expected informatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00549","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/2208.00549/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":"2026-07-05T05:13:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ARm793/ZWtdYcp5LrQIARY50WaNZm7Sp8WAHPQBoqi6oYytk4DVxCrsRvKd07kweVOse5fZago1DKB1HZZ87BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T23:16:03.072766Z"},"content_sha256":"be6be2f9ba1dfed230b27b9e8fc93fad780d42d12b470678c22e3274b75340e7","schema_version":"1.0","event_id":"sha256:be6be2f9ba1dfed230b27b9e8fc93fad780d42d12b470678c22e3274b75340e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EOFE776AEVE7GREU5Z6BIYMBAX/bundle.json","state_url":"https://pith.science/pith/EOFE776AEVE7GREU5Z6BIYMBAX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EOFE776AEVE7GREU5Z6BIYMBAX/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":"2026-08-03T23:16:03Z","links":{"resolver":"https://pith.science/pith/EOFE776AEVE7GREU5Z6BIYMBAX","bundle":"https://pith.science/pith/EOFE776AEVE7GREU5Z6BIYMBAX/bundle.json","state":"https://pith.science/pith/EOFE776AEVE7GREU5Z6BIYMBAX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EOFE776AEVE7GREU5Z6BIYMBAX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EOFE776AEVE7GREU5Z6BIYMBAX","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":"9f8bf6bec6656ecb4c6d32e3dc44890a02bc7b933ac9b14f2bcd5dc6d9fa7d9a","cross_cats_sorted":["cs.AI","cs.IR","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T00:36:57Z","title_canon_sha256":"736f1bdb2408f797c4aecd16c5af5d9f8d8732bdd6d84e3b55e171b5abecf31d"},"schema_version":"1.0","source":{"id":"2208.00549","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.00549","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"arxiv_version","alias_value":"2208.00549v2","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00549","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"pith_short_12","alias_value":"EOFE776AEVE7","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"pith_short_16","alias_value":"EOFE776AEVE7GREU","created_at":"2026-07-05T05:13:36Z"},{"alias_kind":"pith_short_8","alias_value":"EOFE776A","created_at":"2026-07-05T05:13:36Z"}],"graph_snapshots":[{"event_id":"sha256:be6be2f9ba1dfed230b27b9e8fc93fad780d42d12b470678c22e3274b75340e7","target":"graph","created_at":"2026-07-05T05:13:36Z","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/2208.00549/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently proposed methods in data subset selection, that is active learning and active sampling, use Fisher information, Hessians, similarity matrices based on gradients, and gradient lengths to estimate how informative data is for a model's training. Are these different approaches connected, and if so, how? We revisit the fundamentals of Bayesian optimal experiment design and show that these recently proposed methods can be understood as approximations to information-theoretic quantities: among them, the mutual information between predictions and model parameters, known as expected informatio","authors_text":"Andreas Kirsch, Yarin Gal","cross_cats":["cs.AI","cs.IR","cs.IT","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T00:36:57Z","title":"Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00549","kind":"arxiv","version":2},"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":"sha256:1abf53f00d5895c8178dee851f655ac5c50d4a39669a363a005be3ecc8a18ca8","target":"record","created_at":"2026-07-05T05:13:36Z","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":"9f8bf6bec6656ecb4c6d32e3dc44890a02bc7b933ac9b14f2bcd5dc6d9fa7d9a","cross_cats_sorted":["cs.AI","cs.IR","cs.IT","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-08-01T00:36:57Z","title_canon_sha256":"736f1bdb2408f797c4aecd16c5af5d9f8d8732bdd6d84e3b55e171b5abecf31d"},"schema_version":"1.0","source":{"id":"2208.00549","kind":"arxiv","version":2}},"canonical_sha256":"238a4fffc02549f34494ee7c14618105d1c30e8b3226c725a1806566f12d152d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"238a4fffc02549f34494ee7c14618105d1c30e8b3226c725a1806566f12d152d","first_computed_at":"2026-07-05T05:13:36.762289Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:13:36.762289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8FSqyg5F3O4zAk8jwBkBcfOOZaM0BIR0DXEknadp5mkl3Ekq9DzL5G/VgxTZpLLf2FAv/6VnJsbhbHRGwphSAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:13:36.762767Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.00549","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1abf53f00d5895c8178dee851f655ac5c50d4a39669a363a005be3ecc8a18ca8","sha256:be6be2f9ba1dfed230b27b9e8fc93fad780d42d12b470678c22e3274b75340e7"],"state_sha256":"fd24a10d374497ec06095bf49a4f55ec156d2be479e7f25770c285574509c2ae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fdXIdbQNVf2bRMkZMjOT8Qp2Gx2eLwZ+G/UGwR6GR8CBrPvS6SAI7+OAOGksyvKOy/Wc5+Y9rXqOubKhMSMNDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T23:16:03.076675Z","bundle_sha256":"afb0b21cf7238c01523d8a80efd42f90d64cbf9f7f3ffea7af28b2513b941028"}}