{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:V2WM2SG347OQ3VXCGE3PON3MG3","short_pith_number":"pith:V2WM2SG3","canonical_record":{"source":{"id":"1402.5902","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-24T17:40:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d971fdaedd5e8250efb81a052ecc169442308439b3dc017e0486380ebafa4d6a","abstract_canon_sha256":"286d47446140e30b5fb299a6d87f28a9fe5995ba004056bc25a6b217e7b16742"},"schema_version":"1.0"},"canonical_sha256":"aeaccd48dbe7dd0dd6e23136f7376c36e694f5bb9050e2c8f0ef35c38daa6f25","source":{"kind":"arxiv","id":"1402.5902","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.5902","created_at":"2026-05-18T02:27:18Z"},{"alias_kind":"arxiv_version","alias_value":"1402.5902v2","created_at":"2026-05-18T02:27:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.5902","created_at":"2026-05-18T02:27:18Z"},{"alias_kind":"pith_short_12","alias_value":"V2WM2SG347OQ","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_16","alias_value":"V2WM2SG347OQ3VXC","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_8","alias_value":"V2WM2SG3","created_at":"2026-05-18T12:28:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:V2WM2SG347OQ3VXCGE3PON3MG3","target":"record","payload":{"canonical_record":{"source":{"id":"1402.5902","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-24T17:40:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d971fdaedd5e8250efb81a052ecc169442308439b3dc017e0486380ebafa4d6a","abstract_canon_sha256":"286d47446140e30b5fb299a6d87f28a9fe5995ba004056bc25a6b217e7b16742"},"schema_version":"1.0"},"canonical_sha256":"aeaccd48dbe7dd0dd6e23136f7376c36e694f5bb9050e2c8f0ef35c38daa6f25","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:27:18.869795Z","signature_b64":"xckWka250CQ5/njVwWnMqEWl0NF2UqtcJCJGuiBF6NMEfRpB5Xjs3ewJfS4gyxda+RtpcHjoq15JoF+r4KZLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aeaccd48dbe7dd0dd6e23136f7376c36e694f5bb9050e2c8f0ef35c38daa6f25","last_reissued_at":"2026-05-18T02:27:18.869110Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:27:18.869110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1402.5902","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":"2026-05-18T02:27:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5Bqhqbz95Rf/GoxM2bQurUQDfcjYzFMwFBryU3814p+VHI+G6/YEfdnzi71GjlupBodn0soubKmQzbJHhMT6Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:32:31.076290Z"},"content_sha256":"01009fe6325f7a377186fbbc2bd89508dcf3d99c546384d55533f43251258995","schema_version":"1.0","event_id":"sha256:01009fe6325f7a377186fbbc2bd89508dcf3d99c546384d55533f43251258995"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:V2WM2SG347OQ3VXCGE3PON3MG3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Learning from Label Proportions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar, Shih-Fu Chang, Tony Jebara","submitted_at":"2014-02-24T17:40:09Z","abstract_excerpt":"Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or \"bags\", and only the proportion of each class in each bag is known. The task is to learn a model to predict the class labels of the individual instances. LLP has broad applications in political science, marketing, healthcare, and computer vision. This work answers the fundamental question, when and why LLP is possible, by introducing a general framework, Empirical Proportion Risk Minimization (EPRM). EPRM learns an instance label classifier to match the given label proportions on the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.5902","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":""},"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":"2026-05-18T02:27:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AcVanZ0mXdRuWAOq/7BMXJrtP1wXJ5acaAmEkjZlkiVXENM9tnwbB+y5C/cKTU3NolXlGCK3AJ2R3iWAQD2ZAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T16:32:31.076658Z"},"content_sha256":"03b62785de82d548983f41540db21419684d0f83b309a83f7bde3eb7d9200090","schema_version":"1.0","event_id":"sha256:03b62785de82d548983f41540db21419684d0f83b309a83f7bde3eb7d9200090"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V2WM2SG347OQ3VXCGE3PON3MG3/bundle.json","state_url":"https://pith.science/pith/V2WM2SG347OQ3VXCGE3PON3MG3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V2WM2SG347OQ3VXCGE3PON3MG3/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":"2026-05-26T16:32:31Z","links":{"resolver":"https://pith.science/pith/V2WM2SG347OQ3VXCGE3PON3MG3","bundle":"https://pith.science/pith/V2WM2SG347OQ3VXCGE3PON3MG3/bundle.json","state":"https://pith.science/pith/V2WM2SG347OQ3VXCGE3PON3MG3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V2WM2SG347OQ3VXCGE3PON3MG3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:V2WM2SG347OQ3VXCGE3PON3MG3","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":"286d47446140e30b5fb299a6d87f28a9fe5995ba004056bc25a6b217e7b16742","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-24T17:40:09Z","title_canon_sha256":"d971fdaedd5e8250efb81a052ecc169442308439b3dc017e0486380ebafa4d6a"},"schema_version":"1.0","source":{"id":"1402.5902","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1402.5902","created_at":"2026-05-18T02:27:18Z"},{"alias_kind":"arxiv_version","alias_value":"1402.5902v2","created_at":"2026-05-18T02:27:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.5902","created_at":"2026-05-18T02:27:18Z"},{"alias_kind":"pith_short_12","alias_value":"V2WM2SG347OQ","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_16","alias_value":"V2WM2SG347OQ3VXC","created_at":"2026-05-18T12:28:52Z"},{"alias_kind":"pith_short_8","alias_value":"V2WM2SG3","created_at":"2026-05-18T12:28:52Z"}],"graph_snapshots":[{"event_id":"sha256:03b62785de82d548983f41540db21419684d0f83b309a83f7bde3eb7d9200090","target":"graph","created_at":"2026-05-18T02:27:18Z","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"},"paper":{"abstract_excerpt":"Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or \"bags\", and only the proportion of each class in each bag is known. The task is to learn a model to predict the class labels of the individual instances. LLP has broad applications in political science, marketing, healthcare, and computer vision. This work answers the fundamental question, when and why LLP is possible, by introducing a general framework, Empirical Proportion Risk Minimization (EPRM). EPRM learns an instance label classifier to match the given label proportions on the ","authors_text":"Felix X. Yu, Krzysztof Choromanski, Sanjiv Kumar, Shih-Fu Chang, Tony Jebara","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-24T17:40:09Z","title":"On Learning from Label Proportions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.5902","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":"sha256:01009fe6325f7a377186fbbc2bd89508dcf3d99c546384d55533f43251258995","target":"record","created_at":"2026-05-18T02:27:18Z","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":"286d47446140e30b5fb299a6d87f28a9fe5995ba004056bc25a6b217e7b16742","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-02-24T17:40:09Z","title_canon_sha256":"d971fdaedd5e8250efb81a052ecc169442308439b3dc017e0486380ebafa4d6a"},"schema_version":"1.0","source":{"id":"1402.5902","kind":"arxiv","version":2}},"canonical_sha256":"aeaccd48dbe7dd0dd6e23136f7376c36e694f5bb9050e2c8f0ef35c38daa6f25","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aeaccd48dbe7dd0dd6e23136f7376c36e694f5bb9050e2c8f0ef35c38daa6f25","first_computed_at":"2026-05-18T02:27:18.869110Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:27:18.869110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xckWka250CQ5/njVwWnMqEWl0NF2UqtcJCJGuiBF6NMEfRpB5Xjs3ewJfS4gyxda+RtpcHjoq15JoF+r4KZLCw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:27:18.869795Z","signed_message":"canonical_sha256_bytes"},"source_id":"1402.5902","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01009fe6325f7a377186fbbc2bd89508dcf3d99c546384d55533f43251258995","sha256:03b62785de82d548983f41540db21419684d0f83b309a83f7bde3eb7d9200090"],"state_sha256":"8e5a04a13c8de7cd3e26db9d01308b7ccf19a60133b6e30459a3ceac8f1bd12d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UkDeGfBE8Oy5+0oXB/7VaWV1E8lzqTy6rtRbOQOXtSUei4TO5h3SJWRSSKR7WmsMi7gu804NfWiv1z+2UvknDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T16:32:31.079076Z","bundle_sha256":"5b0ac44fb15d56d3022dd50737a056094720525f4dafeb1f56e630c053f8d110"}}