{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WTWVTI7DL2PX3PZOQ45GCVJXWP","short_pith_number":"pith:WTWVTI7D","schema_version":"1.0","canonical_sha256":"b4ed59a3e35e9f7dbf2e873a615537b3cda65bea31605659490084101551ff18","source":{"kind":"arxiv","id":"2006.12204","version":2},"attestation_state":"computed","paper":{"title":"Telescoping Density-Ratio Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Benjamin Rhodes, Kai Xu, Michael U. Gutmann","submitted_at":"2020-06-22T12:55:06Z","abstract_excerpt":"Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and generative modelling, with the number of use-cases continuing to proliferate. However, it suffers from a critical limitation: it fails to accurately estimate ratios p/q for which the two densities differ significantly. Empirically, we find this occurs whenever the KL divergence between p and q exceeds tens of nats. To resolve this limitation, we introduce a new framework, telescoping density-ratio estimation (TRE), tha"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2006.12204","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-22T12:55:06Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"50a651157d760402ffbc40b667e115e4a0b117d469e120bdee79b744f53adc9d","abstract_canon_sha256":"29c1b23c99d08c11e9a28b37e7450f98f465c7013a69ddeca7b68a164bbee8d6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:53:52.311736Z","signature_b64":"5DV/iCbi98pLFGrfGwRxShwm3I89IBWfniPiIOyc3HLyOEUlftD7zQSGm+l+QPwOygXjJw8NSfDQYpQzx9FrDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4ed59a3e35e9f7dbf2e873a615537b3cda65bea31605659490084101551ff18","last_reissued_at":"2026-07-05T01:53:52.311333Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:53:52.311333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Telescoping Density-Ratio Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Benjamin Rhodes, Kai Xu, Michael U. Gutmann","submitted_at":"2020-06-22T12:55:06Z","abstract_excerpt":"Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and generative modelling, with the number of use-cases continuing to proliferate. However, it suffers from a critical limitation: it fails to accurately estimate ratios p/q for which the two densities differ significantly. Empirically, we find this occurs whenever the KL divergence between p and q exceeds tens of nats. To resolve this limitation, we introduce a new framework, telescoping density-ratio estimation (TRE), tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.12204","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.12204/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2006.12204","created_at":"2026-07-05T01:53:52.311388+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.12204v2","created_at":"2026-07-05T01:53:52.311388+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.12204","created_at":"2026-07-05T01:53:52.311388+00:00"},{"alias_kind":"pith_short_12","alias_value":"WTWVTI7DL2PX","created_at":"2026-07-05T01:53:52.311388+00:00"},{"alias_kind":"pith_short_16","alias_value":"WTWVTI7DL2PX3PZO","created_at":"2026-07-05T01:53:52.311388+00:00"},{"alias_kind":"pith_short_8","alias_value":"WTWVTI7D","created_at":"2026-07-05T01:53:52.311388+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.02219","citing_title":"Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP","json":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP.json","graph_json":"https://pith.science/api/pith-number/WTWVTI7DL2PX3PZOQ45GCVJXWP/graph.json","events_json":"https://pith.science/api/pith-number/WTWVTI7DL2PX3PZOQ45GCVJXWP/events.json","paper":"https://pith.science/paper/WTWVTI7D"},"agent_actions":{"view_html":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP","download_json":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP.json","view_paper":"https://pith.science/paper/WTWVTI7D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.12204&json=true","fetch_graph":"https://pith.science/api/pith-number/WTWVTI7DL2PX3PZOQ45GCVJXWP/graph.json","fetch_events":"https://pith.science/api/pith-number/WTWVTI7DL2PX3PZOQ45GCVJXWP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP/action/storage_attestation","attest_author":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP/action/author_attestation","sign_citation":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP/action/citation_signature","submit_replication":"https://pith.science/pith/WTWVTI7DL2PX3PZOQ45GCVJXWP/action/replication_record"}},"created_at":"2026-07-05T01:53:52.311388+00:00","updated_at":"2026-07-05T01:53:52.311388+00:00"}