{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:5IN52FJU6XH2KHG2H4RURINKFL","short_pith_number":"pith:5IN52FJU","canonical_record":{"source":{"id":"1603.02501","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-08T12:43:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8fdd68ab339256909dae6cec8daf8ca3026d0c7445c7a324f7b3951dc779fdf6","abstract_canon_sha256":"0100603e4496fa971f0177432937c5fcfbe3c1b844acb337fd15677f6190193e"},"schema_version":"1.0"},"canonical_sha256":"ea1bdd1534f5cfa51cda3f2348a1aa2ad684dc74726fec767fc8dcb73e162ff8","source":{"kind":"arxiv","id":"1603.02501","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.02501","created_at":"2026-05-18T01:13:15Z"},{"alias_kind":"arxiv_version","alias_value":"1603.02501v2","created_at":"2026-05-18T01:13:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.02501","created_at":"2026-05-18T01:13:15Z"},{"alias_kind":"pith_short_12","alias_value":"5IN52FJU6XH2","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"5IN52FJU6XH2KHG2","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"5IN52FJU","created_at":"2026-05-18T12:30:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:5IN52FJU6XH2KHG2H4RURINKFL","target":"record","payload":{"canonical_record":{"source":{"id":"1603.02501","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-08T12:43:29Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"8fdd68ab339256909dae6cec8daf8ca3026d0c7445c7a324f7b3951dc779fdf6","abstract_canon_sha256":"0100603e4496fa971f0177432937c5fcfbe3c1b844acb337fd15677f6190193e"},"schema_version":"1.0"},"canonical_sha256":"ea1bdd1534f5cfa51cda3f2348a1aa2ad684dc74726fec767fc8dcb73e162ff8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:13:15.001227Z","signature_b64":"2TTLGTURckG0lpb7f8NEaXZxwjpFCbwfB1aZV/s7qsc2ApRGqnF0u7BkAnkyBmlFGnvi84nrqIb8khAk1bLtCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea1bdd1534f5cfa51cda3f2348a1aa2ad684dc74726fec767fc8dcb73e162ff8","last_reissued_at":"2026-05-18T01:13:15.000851Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:13:15.000851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.02501","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":"2026-05-18T01:13:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/gI6VTHOHJCY7/v89YOwiEkbMbBBlibX5lyK0Pj3zsn66+AMrYACyO8gSO8X7O7iGzDBUNnabvAbsTqisG71DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:01:27.188921Z"},"content_sha256":"138d3a7dc66392a7cc6ea1bed5c47bb2228f412eb1ffc07e8c697185c62c3aef","schema_version":"1.0","event_id":"sha256:138d3a7dc66392a7cc6ea1bed5c47bb2228f412eb1ffc07e8c697185c62c3aef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:5IN52FJU6XH2KHG2H4RURINKFL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mixture Proportion Estimation via Kernel Embedding of Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ambuj Tewari, Clayton Scott, Harish G. Ramaswamy","submitted_at":"2016-03-08T12:43:29Z","abstract_excerpt":"Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem constitutes a key part in many \"weakly supervised learning\" problems like learning with positive and unlabelled samples, learning with label noise, anomaly detection and crowdsourcing. While there have been several methods proposed to solve this problem, to the best of our knowledge no efficient algorithm with a proven convergence rate towards the true proportion exists for this problem. We fill this gap by constructing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.02501","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":"2026-05-18T01:13:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"igVKCXaRMuR1Bms8d365nJtzF0xR+LPewamUWIyHl8QoKriaAoN+8X7wmEFtsE+0kUi79bQgAjPJR5WLbmKrDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:01:27.189644Z"},"content_sha256":"1dd2607eeb00c6a8948723aaa4a545d6fdaf0c374c14ebfa14f3ce243dc96140","schema_version":"1.0","event_id":"sha256:1dd2607eeb00c6a8948723aaa4a545d6fdaf0c374c14ebfa14f3ce243dc96140"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5IN52FJU6XH2KHG2H4RURINKFL/bundle.json","state_url":"https://pith.science/pith/5IN52FJU6XH2KHG2H4RURINKFL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5IN52FJU6XH2KHG2H4RURINKFL/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":"2026-05-27T11:01:27Z","links":{"resolver":"https://pith.science/pith/5IN52FJU6XH2KHG2H4RURINKFL","bundle":"https://pith.science/pith/5IN52FJU6XH2KHG2H4RURINKFL/bundle.json","state":"https://pith.science/pith/5IN52FJU6XH2KHG2H4RURINKFL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5IN52FJU6XH2KHG2H4RURINKFL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:5IN52FJU6XH2KHG2H4RURINKFL","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":"0100603e4496fa971f0177432937c5fcfbe3c1b844acb337fd15677f6190193e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-08T12:43:29Z","title_canon_sha256":"8fdd68ab339256909dae6cec8daf8ca3026d0c7445c7a324f7b3951dc779fdf6"},"schema_version":"1.0","source":{"id":"1603.02501","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.02501","created_at":"2026-05-18T01:13:15Z"},{"alias_kind":"arxiv_version","alias_value":"1603.02501v2","created_at":"2026-05-18T01:13:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.02501","created_at":"2026-05-18T01:13:15Z"},{"alias_kind":"pith_short_12","alias_value":"5IN52FJU6XH2","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_16","alias_value":"5IN52FJU6XH2KHG2","created_at":"2026-05-18T12:30:01Z"},{"alias_kind":"pith_short_8","alias_value":"5IN52FJU","created_at":"2026-05-18T12:30:01Z"}],"graph_snapshots":[{"event_id":"sha256:1dd2607eeb00c6a8948723aaa4a545d6fdaf0c374c14ebfa14f3ce243dc96140","target":"graph","created_at":"2026-05-18T01:13:15Z","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":"Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem constitutes a key part in many \"weakly supervised learning\" problems like learning with positive and unlabelled samples, learning with label noise, anomaly detection and crowdsourcing. While there have been several methods proposed to solve this problem, to the best of our knowledge no efficient algorithm with a proven convergence rate towards the true proportion exists for this problem. We fill this gap by constructing","authors_text":"Ambuj Tewari, Clayton Scott, Harish G. Ramaswamy","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-08T12:43:29Z","title":"Mixture Proportion Estimation via Kernel Embedding of Distributions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.02501","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":"sha256:138d3a7dc66392a7cc6ea1bed5c47bb2228f412eb1ffc07e8c697185c62c3aef","target":"record","created_at":"2026-05-18T01:13:15Z","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":"0100603e4496fa971f0177432937c5fcfbe3c1b844acb337fd15677f6190193e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-03-08T12:43:29Z","title_canon_sha256":"8fdd68ab339256909dae6cec8daf8ca3026d0c7445c7a324f7b3951dc779fdf6"},"schema_version":"1.0","source":{"id":"1603.02501","kind":"arxiv","version":2}},"canonical_sha256":"ea1bdd1534f5cfa51cda3f2348a1aa2ad684dc74726fec767fc8dcb73e162ff8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ea1bdd1534f5cfa51cda3f2348a1aa2ad684dc74726fec767fc8dcb73e162ff8","first_computed_at":"2026-05-18T01:13:15.000851Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:13:15.000851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2TTLGTURckG0lpb7f8NEaXZxwjpFCbwfB1aZV/s7qsc2ApRGqnF0u7BkAnkyBmlFGnvi84nrqIb8khAk1bLtCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:13:15.001227Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.02501","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:138d3a7dc66392a7cc6ea1bed5c47bb2228f412eb1ffc07e8c697185c62c3aef","sha256:1dd2607eeb00c6a8948723aaa4a545d6fdaf0c374c14ebfa14f3ce243dc96140"],"state_sha256":"d3caa50da35a3de5a924cd5b6e8ce9bc14e86f3b9756621acca1cbcb5aa86ec8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8UiyNQ7IbpalmpQco8KqWJU5CueYT8sDNT2gK4a1wQwTu2cMduZh9pqXyxyyGAh1okadcZgi7+A0aUYmd2lwCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:01:27.193615Z","bundle_sha256":"bfa2f57ba3821992e17f67b1c51045138665b7cddbded6993ee0974e03318c0c"}}