{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3RNO3JHJQOS5L5MBISJVMIN2TK","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":"5530e412189e72cc049c1db94183088316f3c734ca38f92f6f800bd51d3d3357","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-04-16T18:53:42Z","title_canon_sha256":"f2d0da98bc46bb4a23039364373140a6b3eac1d34b432574cd351bf66bdd001d"},"schema_version":"1.0","source":{"id":"2104.08312","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.08312","created_at":"2026-07-05T02:32:50Z"},{"alias_kind":"arxiv_version","alias_value":"2104.08312v1","created_at":"2026-07-05T02:32:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.08312","created_at":"2026-07-05T02:32:50Z"},{"alias_kind":"pith_short_12","alias_value":"3RNO3JHJQOS5","created_at":"2026-07-05T02:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"3RNO3JHJQOS5L5MB","created_at":"2026-07-05T02:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"3RNO3JHJ","created_at":"2026-07-05T02:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:6751646ae174a3272009aa9a9fb42637ff970ae8ec45a768df441c4353f071b9","target":"graph","created_at":"2026-07-05T02:32:50Z","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/2104.08312/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Annotating the right set of data amongst all available data points is a key challenge in many machine learning applications. Batch active learning is a popular approach to address this, in which batches of unlabeled data points are selected for annotation, while an underlying learning algorithm gets subsequently updated. Increasingly larger batches are particularly appealing in settings where data can be annotated in parallel, and model training is computationally expensive. A key challenge here is scale - typical active learning methods rely on diversity techniques, which select a diverse set","authors_text":"Amirata Ghorbani, Andre Esteva, James Zou","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-04-16T18:53:42Z","title":"Data Shapley Valuation for Efficient Batch Active Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.08312","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:ea6e29c9fa60d96eb5894d3169b00a98c27565986a6829416e60600ab7b24a83","target":"record","created_at":"2026-07-05T02:32:50Z","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":"5530e412189e72cc049c1db94183088316f3c734ca38f92f6f800bd51d3d3357","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-04-16T18:53:42Z","title_canon_sha256":"f2d0da98bc46bb4a23039364373140a6b3eac1d34b432574cd351bf66bdd001d"},"schema_version":"1.0","source":{"id":"2104.08312","kind":"arxiv","version":1}},"canonical_sha256":"dc5aeda4e983a5d5f58144935621ba9a8030a49563b637bd69e7805d45d0893f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dc5aeda4e983a5d5f58144935621ba9a8030a49563b637bd69e7805d45d0893f","first_computed_at":"2026-07-05T02:32:50.656938Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:32:50.656938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"E3Xbjgqgyd8LG7s6O0mxLZP0OGDQs4e4/e885sAPJelLBujSQdKS64CktT2qEo6j0Ny580E36wGoSOgx9GxUDw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:32:50.657525Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.08312","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea6e29c9fa60d96eb5894d3169b00a98c27565986a6829416e60600ab7b24a83","sha256:6751646ae174a3272009aa9a9fb42637ff970ae8ec45a768df441c4353f071b9"],"state_sha256":"5800d43d1ee1fcf46868a79be4f47f55a6b333ed857b16ae4af75c9e27c235ee"}