{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:GNM6ZILR2CLVEUFK6GAR6FECHZ","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":"33e9bf0105ed6853cd3d59a7660c3e366b653683c7a27d681539996a733e3197","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:09:26Z","title_canon_sha256":"920a6c58038a712fae367eea37d7aece73b01659f95a30462c44958b8162630a"},"schema_version":"1.0","source":{"id":"2310.08056","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.08056","created_at":"2026-07-05T07:58:24Z"},{"alias_kind":"arxiv_version","alias_value":"2310.08056v4","created_at":"2026-07-05T07:58:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.08056","created_at":"2026-07-05T07:58:24Z"},{"alias_kind":"pith_short_12","alias_value":"GNM6ZILR2CLV","created_at":"2026-07-05T07:58:24Z"},{"alias_kind":"pith_short_16","alias_value":"GNM6ZILR2CLVEUFK","created_at":"2026-07-05T07:58:24Z"},{"alias_kind":"pith_short_8","alias_value":"GNM6ZILR","created_at":"2026-07-05T07:58:24Z"}],"graph_snapshots":[{"event_id":"sha256:2927268ddec321173c3a070431c751ef3a34025d363021639c85b799b2c0abd3","target":"graph","created_at":"2026-07-05T07:58:24Z","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/2310.08056/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning from Label Proportions (LLP) is a learning problem where only aggregate level labels are available for groups of instances, called bags, during training, and the aim is to get the best performance at the instance-level on the test data. This setting arises in domains like advertising and medicine due to privacy considerations. We propose a novel algorithmic framework for this problem that iteratively performs two main steps. For the first step (Pseudo Labeling) in every iteration, we define a Gibbs distribution over binary instance labels that incorporates a) covariate information thr","authors_text":"Aravindan Raghuveer, Karthikeyan Shanmugam, Navodita Sharma, Shreyas Havaldar, Shubhi Sareen","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:09:26Z","title":"Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.08056","kind":"arxiv","version":4},"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":"sha256:1bccbecf69584448c88c72dbc6abaf6fa79b85f9d6acbe47e359aab05a49e51b","target":"record","created_at":"2026-07-05T07:58:24Z","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":"33e9bf0105ed6853cd3d59a7660c3e366b653683c7a27d681539996a733e3197","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-10-12T06:09:26Z","title_canon_sha256":"920a6c58038a712fae367eea37d7aece73b01659f95a30462c44958b8162630a"},"schema_version":"1.0","source":{"id":"2310.08056","kind":"arxiv","version":4}},"canonical_sha256":"3359eca171d0975250aaf1811f14823e40e2f71c7b1bbc2cb937cfd2d25d637e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3359eca171d0975250aaf1811f14823e40e2f71c7b1bbc2cb937cfd2d25d637e","first_computed_at":"2026-07-05T07:58:24.058134Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:58:24.058134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7JrocyIeVGAlubfYH4vazjZrvPDXPPRkNS78tAKzpodLe9/G35Vs3F7sUx5gysUjaqXr/HrnLcvRfFuU3A96AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:58:24.058581Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.08056","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1bccbecf69584448c88c72dbc6abaf6fa79b85f9d6acbe47e359aab05a49e51b","sha256:2927268ddec321173c3a070431c751ef3a34025d363021639c85b799b2c0abd3"],"state_sha256":"b887bc360d696fcfe1e3aadf8ea5a4d9c0277501c75097d6acd509bab7ba3085"}