{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:VSBMIV7OA5XVOUTNHEKYF2V3DP","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":"edeb925284d54cc61c728235fa73e970f89b6e8e384bccee50eb8d3f810fc9d6","cross_cats_sorted":["cs.IT","cs.LG","math.IT","math.OC","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-14T16:46:16Z","title_canon_sha256":"93312a554306151b6f546be75aaa3a75479f2658ffd0586ed24c6661d217f5db"},"schema_version":"1.0","source":{"id":"2006.07953","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.07953","created_at":"2026-07-05T01:49:52Z"},{"alias_kind":"arxiv_version","alias_value":"2006.07953v2","created_at":"2026-07-05T01:49:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.07953","created_at":"2026-07-05T01:49:52Z"},{"alias_kind":"pith_short_12","alias_value":"VSBMIV7OA5XV","created_at":"2026-07-05T01:49:52Z"},{"alias_kind":"pith_short_16","alias_value":"VSBMIV7OA5XVOUTN","created_at":"2026-07-05T01:49:52Z"},{"alias_kind":"pith_short_8","alias_value":"VSBMIV7O","created_at":"2026-07-05T01:49:52Z"}],"graph_snapshots":[{"event_id":"sha256:05f01482f24bff3b4a00fdf570502ec79ce636429bc8cd2d800031c966b409c4","target":"graph","created_at":"2026-07-05T01:49:52Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2006.07953/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many problems in statistics and machine learning require the reconstruction of a rank-one signal matrix from noisy data. Enforcing additional prior information on the rank-one component is often key to guaranteeing good recovery performance. One such prior on the low-rank component is sparsity, giving rise to the sparse principal component analysis problem. Unfortunately, there is strong evidence that this problem suffers from a computational-to-statistical gap, which may be fundamental. In this work, we study an alternative prior where the low-rank component is in the range of a trained gener","authors_text":"Jorio Cocola, Paul Hand, Vladislav Voroninski","cross_cats":["cs.IT","cs.LG","math.IT","math.OC","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-14T16:46:16Z","title":"Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.07953","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":"sha256:434093f5f7ad742016e3cd88372aa694b59cd470e9bf67a24c3a774a70a91f41","target":"record","created_at":"2026-07-05T01:49:52Z","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":"edeb925284d54cc61c728235fa73e970f89b6e8e384bccee50eb8d3f810fc9d6","cross_cats_sorted":["cs.IT","cs.LG","math.IT","math.OC","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-14T16:46:16Z","title_canon_sha256":"93312a554306151b6f546be75aaa3a75479f2658ffd0586ed24c6661d217f5db"},"schema_version":"1.0","source":{"id":"2006.07953","kind":"arxiv","version":2}},"canonical_sha256":"ac82c457ee076f57526d391582eabb1bc19ed13cce2a2facbb9ec31ef38fab7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ac82c457ee076f57526d391582eabb1bc19ed13cce2a2facbb9ec31ef38fab7e","first_computed_at":"2026-07-05T01:49:52.613919Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:52.613919Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"F3HJIvl7VpxB+dHuwbVCE1qPratJgqzxy9N7/bQs23jgOf3A5n3nL1jLNdv4Jl2FYAqYlm5d3du55GviAR0BAw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:52.614438Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.07953","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:434093f5f7ad742016e3cd88372aa694b59cd470e9bf67a24c3a774a70a91f41","sha256:05f01482f24bff3b4a00fdf570502ec79ce636429bc8cd2d800031c966b409c4"],"state_sha256":"bde3ca1b7fbf066ee701772720da40646d82b755d6ea4b1fcc1b0842f113674e"}