{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:O23ZBLPUZIQUIADBTVXEKZIATJ","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":"a8940002f42e2b96087ea51a7d166d4a2b59873eda112f16f369d65a173f3f6b","cross_cats_sorted":["cs.CC","cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T17:25:23Z","title_canon_sha256":"57c7c959d93070ecefbecae7a9f850c901a5c9d3773706700b0c7f5264436f79"},"schema_version":"1.0","source":{"id":"2402.07821","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.07821","created_at":"2026-07-05T08:28:59Z"},{"alias_kind":"arxiv_version","alias_value":"2402.07821v2","created_at":"2026-07-05T08:28:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07821","created_at":"2026-07-05T08:28:59Z"},{"alias_kind":"pith_short_12","alias_value":"O23ZBLPUZIQU","created_at":"2026-07-05T08:28:59Z"},{"alias_kind":"pith_short_16","alias_value":"O23ZBLPUZIQUIADB","created_at":"2026-07-05T08:28:59Z"},{"alias_kind":"pith_short_8","alias_value":"O23ZBLPU","created_at":"2026-07-05T08:28:59Z"}],"graph_snapshots":[{"event_id":"sha256:be13370b70624478f0ab966fb2d62f244320e424e1002bc955cd0c506263af02","target":"graph","created_at":"2026-07-05T08:28:59Z","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/2402.07821/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Consider a multi-class labelling problem, where the labels can take values in $[k]$, and a predictor predicts a distribution over the labels. In this work, we study the following foundational question: Are there notions of multi-class calibration that give strong guarantees of meaningful predictions and can be achieved in time and sample complexities polynomial in $k$? Prior notions of calibration exhibit a tradeoff between computational efficiency and expressivity: they either suffer from having sample complexity exponential in $k$, or needing to solve computationally intractable problems, or","authors_text":"Guy N. Rothblum, Lunjia Hu, Parikshit Gopalan","cross_cats":["cs.CC","cs.DS","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T17:25:23Z","title":"On Computationally Efficient Multi-Class Calibration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07821","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":"sha256:0238b8b15a62d6b4ae9d619c9c1c00ed13a0118b87c7433653b96e6084ce0791","target":"record","created_at":"2026-07-05T08:28:59Z","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":"a8940002f42e2b96087ea51a7d166d4a2b59873eda112f16f369d65a173f3f6b","cross_cats_sorted":["cs.CC","cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T17:25:23Z","title_canon_sha256":"57c7c959d93070ecefbecae7a9f850c901a5c9d3773706700b0c7f5264436f79"},"schema_version":"1.0","source":{"id":"2402.07821","kind":"arxiv","version":2}},"canonical_sha256":"76b790adf4ca214400619d6e4565009a5a8700328d507430935a9e317ea42eb9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"76b790adf4ca214400619d6e4565009a5a8700328d507430935a9e317ea42eb9","first_computed_at":"2026-07-05T08:28:59.417293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:28:59.417293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a/3emtUo2KD9JLesujkjGC2aRuebd0xzwmpymAArjaNoPqSEq3YwgUM2iw/yP0GBAsCpobUiKpn5YqaObBUlBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:28:59.417772Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.07821","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0238b8b15a62d6b4ae9d619c9c1c00ed13a0118b87c7433653b96e6084ce0791","sha256:be13370b70624478f0ab966fb2d62f244320e424e1002bc955cd0c506263af02"],"state_sha256":"6203f2b83e36edc5099809f023d710a7f3e01cda9cfa761b838abf1f1a9b3023"}