{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LICBJAAQWWG6SRM4JUE6DNB2BL","short_pith_number":"pith:LICBJAAQ","schema_version":"1.0","canonical_sha256":"5a04148010b58de9459c4d09e1b43a0ae7663312ef02e62f79bf791ab4f521fb","source":{"kind":"arxiv","id":"1910.12774","version":2},"attestation_state":"computed","paper":{"title":"Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"George H. Chen, Wei Ma","submitted_at":"2019-10-28T16:01:47Z","abstract_excerpt":"Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can yield overly optimistic predictions of unseen user ratings. Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing. These probabilities are typically modeled using logistic "},"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":"1910.12774","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-10-28T16:01:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"9f5bd3d1bde141d5f19161dab72dc1d410f116466f632c40fd31a36a6c7df5fc","abstract_canon_sha256":"335cd6982bc96ceb9549c481611cab889a5cc4fb89f6db3e30ec975b6c990317"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:15:42.020385Z","signature_b64":"OINM1+MTBT2UuG90qkq6C/A5GX88Zq2fhe0adA/eMpRMy9FlQOpgz+eAjBK+b/noGtptHItQ0D1wAjGtRqk4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5a04148010b58de9459c4d09e1b43a0ae7663312ef02e62f79bf791ab4f521fb","last_reissued_at":"2026-07-05T00:15:42.019905Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:15:42.019905Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"George H. Chen, Wei Ma","submitted_at":"2019-10-28T16:01:47Z","abstract_excerpt":"Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can yield overly optimistic predictions of unseen user ratings. Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing. These probabilities are typically modeled using logistic "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.12774","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/1910.12774/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":"1910.12774","created_at":"2026-07-05T00:15:42.019957+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.12774v2","created_at":"2026-07-05T00:15:42.019957+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.12774","created_at":"2026-07-05T00:15:42.019957+00:00"},{"alias_kind":"pith_short_12","alias_value":"LICBJAAQWWG6","created_at":"2026-07-05T00:15:42.019957+00:00"},{"alias_kind":"pith_short_16","alias_value":"LICBJAAQWWG6SRM4","created_at":"2026-07-05T00:15:42.019957+00:00"},{"alias_kind":"pith_short_8","alias_value":"LICBJAAQ","created_at":"2026-07-05T00:15:42.019957+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.12965","citing_title":"Two-Sided Nearest Neighbors: An adaptive and minimax optimal procedure for matrix completion","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL","json":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL.json","graph_json":"https://pith.science/api/pith-number/LICBJAAQWWG6SRM4JUE6DNB2BL/graph.json","events_json":"https://pith.science/api/pith-number/LICBJAAQWWG6SRM4JUE6DNB2BL/events.json","paper":"https://pith.science/paper/LICBJAAQ"},"agent_actions":{"view_html":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL","download_json":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL.json","view_paper":"https://pith.science/paper/LICBJAAQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.12774&json=true","fetch_graph":"https://pith.science/api/pith-number/LICBJAAQWWG6SRM4JUE6DNB2BL/graph.json","fetch_events":"https://pith.science/api/pith-number/LICBJAAQWWG6SRM4JUE6DNB2BL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL/action/storage_attestation","attest_author":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL/action/author_attestation","sign_citation":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL/action/citation_signature","submit_replication":"https://pith.science/pith/LICBJAAQWWG6SRM4JUE6DNB2BL/action/replication_record"}},"created_at":"2026-07-05T00:15:42.019957+00:00","updated_at":"2026-07-05T00:15:42.019957+00:00"}