{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:NAIDGAGKSUSQIRCTYPVFTZRS7R","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":"412f4e2d6192072fc2fe47198a472e31df9555bcf9766369819c7636359d8818","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-03-16T17:00:35Z","title_canon_sha256":"d44e8bfd14cdaa32589643b588fe27640cb4b3e6cd5f9cd4e2faafb941363ffe"},"schema_version":"1.0","source":{"id":"2003.07329","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.07329","created_at":"2026-07-05T01:14:42Z"},{"alias_kind":"arxiv_version","alias_value":"2003.07329v2","created_at":"2026-07-05T01:14:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.07329","created_at":"2026-07-05T01:14:42Z"},{"alias_kind":"pith_short_12","alias_value":"NAIDGAGKSUSQ","created_at":"2026-07-05T01:14:42Z"},{"alias_kind":"pith_short_16","alias_value":"NAIDGAGKSUSQIRCT","created_at":"2026-07-05T01:14:42Z"},{"alias_kind":"pith_short_8","alias_value":"NAIDGAGK","created_at":"2026-07-05T01:14:42Z"}],"graph_snapshots":[{"event_id":"sha256:d426eb981fd3153de3f3e782ae5ec8d564c113899aa2d14f56f695369810e679","target":"graph","created_at":"2026-07-05T01:14:42Z","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/2003.07329/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high expressive power. We show that none of the existing methods satisfy all three requirements, and demonstrate how Mix-n-Match calibration strategies (i.e., ensemble and composition) can help achieve remarkably better data-efficiency and expressive power while provably maintaining the classification accuracy of the original classifier. Mix-n-Match strategies are generic in the sense that","authors_text":"Bhavya Kailkhura, Jize Zhang, T. Yong-Jin Han","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-03-16T17:00:35Z","title":"Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.07329","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:e33629c2bcbcd37940db28cca23532c894547eaabcd69f809b5eb0d23dab95f6","target":"record","created_at":"2026-07-05T01:14:42Z","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":"412f4e2d6192072fc2fe47198a472e31df9555bcf9766369819c7636359d8818","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2020-03-16T17:00:35Z","title_canon_sha256":"d44e8bfd14cdaa32589643b588fe27640cb4b3e6cd5f9cd4e2faafb941363ffe"},"schema_version":"1.0","source":{"id":"2003.07329","kind":"arxiv","version":2}},"canonical_sha256":"68103300ca9525044453c3ea59e632fc55dc960143817119b38f1f3a4f67c356","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"68103300ca9525044453c3ea59e632fc55dc960143817119b38f1f3a4f67c356","first_computed_at":"2026-07-05T01:14:42.321388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:14:42.321388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"INKnbnemYETY0UItnK3qKdDcIPcIWZ8ud1XGzvlMTLpQRzcL/UpR9ug+s2v/mt0QEeqfbxSUPw+n1TvO3vMpBA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:14:42.321907Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.07329","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e33629c2bcbcd37940db28cca23532c894547eaabcd69f809b5eb0d23dab95f6","sha256:d426eb981fd3153de3f3e782ae5ec8d564c113899aa2d14f56f695369810e679"],"state_sha256":"c4c9e5a0acaa687e5eae755370a1b1944b29ff85cbf892a40043ea2539fc3df5"}