{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:25VCPZVA2MWKPD3AB7XHK6UG2Y","short_pith_number":"pith:25VCPZVA","schema_version":"1.0","canonical_sha256":"d76a27e6a0d32ca78f600fee757a86d62d60930a7a1d30e6b1b2ab4c524e00df","source":{"kind":"arxiv","id":"2205.14461","version":2},"attestation_state":"computed","paper":{"title":"Collaborative likelihood-ratio estimation over graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis","submitted_at":"2022-05-28T15:37:03Z","abstract_excerpt":"Assuming we have iid observations from two unknown probability density functions (pdfs), $p$ and $q$, the likelihood-ratio estimation (LRE) is an elegant approach to compare the two pdfs only by relying on the available data. In this paper, we introduce the first -to the best of our knowledge-graph-based extension of this problem, which reads as follows: Suppose each node $v$ of a fixed graph has access to observations coming from two unknown node-specific pdfs, $p_v$ and $q_v$, and the goal is to estimate for each node the likelihood-ratio between both pdfs by also taking into account the inf"},"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":"2205.14461","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-05-28T15:37:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"06852c5659d4a5bf4d114c4f614918fef12ba7cbe577a36092de297e8682a3d3","abstract_canon_sha256":"033d399db7e3618153c603f8b969177b925cb56d7f3fc043d0ed191c70ee0149"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:39:56.001718Z","signature_b64":"iWNxHdkXIgJVrS645jf4gmQZhk8f8XlOE7p/6/NXsciN2udUew9JQApV/N+uNbJlGH7T0n30TfArQXiDUh7bCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d76a27e6a0d32ca78f600fee757a86d62d60930a7a1d30e6b1b2ab4c524e00df","last_reissued_at":"2026-07-05T07:39:56.001243Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:39:56.001243Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Collaborative likelihood-ratio estimation over graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis","submitted_at":"2022-05-28T15:37:03Z","abstract_excerpt":"Assuming we have iid observations from two unknown probability density functions (pdfs), $p$ and $q$, the likelihood-ratio estimation (LRE) is an elegant approach to compare the two pdfs only by relying on the available data. In this paper, we introduce the first -to the best of our knowledge-graph-based extension of this problem, which reads as follows: Suppose each node $v$ of a fixed graph has access to observations coming from two unknown node-specific pdfs, $p_v$ and $q_v$, and the goal is to estimate for each node the likelihood-ratio between both pdfs by also taking into account the inf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.14461","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/2205.14461/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":"2205.14461","created_at":"2026-07-05T07:39:56.001300+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.14461v2","created_at":"2026-07-05T07:39:56.001300+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.14461","created_at":"2026-07-05T07:39:56.001300+00:00"},{"alias_kind":"pith_short_12","alias_value":"25VCPZVA2MWK","created_at":"2026-07-05T07:39:56.001300+00:00"},{"alias_kind":"pith_short_16","alias_value":"25VCPZVA2MWKPD3A","created_at":"2026-07-05T07:39:56.001300+00:00"},{"alias_kind":"pith_short_8","alias_value":"25VCPZVA","created_at":"2026-07-05T07:39:56.001300+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y","json":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y.json","graph_json":"https://pith.science/api/pith-number/25VCPZVA2MWKPD3AB7XHK6UG2Y/graph.json","events_json":"https://pith.science/api/pith-number/25VCPZVA2MWKPD3AB7XHK6UG2Y/events.json","paper":"https://pith.science/paper/25VCPZVA"},"agent_actions":{"view_html":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y","download_json":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y.json","view_paper":"https://pith.science/paper/25VCPZVA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.14461&json=true","fetch_graph":"https://pith.science/api/pith-number/25VCPZVA2MWKPD3AB7XHK6UG2Y/graph.json","fetch_events":"https://pith.science/api/pith-number/25VCPZVA2MWKPD3AB7XHK6UG2Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y/action/storage_attestation","attest_author":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y/action/author_attestation","sign_citation":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y/action/citation_signature","submit_replication":"https://pith.science/pith/25VCPZVA2MWKPD3AB7XHK6UG2Y/action/replication_record"}},"created_at":"2026-07-05T07:39:56.001300+00:00","updated_at":"2026-07-05T07:39:56.001300+00:00"}