{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:ZU67HMLNG2MAILDCFOUDFLCEK7","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":"490a815747320dec54b349c0876c6ff19466c640de697dddcbdd2f9991a8b1fd","cross_cats_sorted":["cs.LG","math.OC","stat.CO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2020-06-19T15:17:57Z","title_canon_sha256":"ac0bb0d44300d62486cb69ea7ce55d70da2ac73add94554fa906c894620b80d8"},"schema_version":"1.0","source":{"id":"2006.11182","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2006.11182","created_at":"2026-07-05T01:11:36Z"},{"alias_kind":"arxiv_version","alias_value":"2006.11182v1","created_at":"2026-07-05T01:11:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.11182","created_at":"2026-07-05T01:11:36Z"},{"alias_kind":"pith_short_12","alias_value":"ZU67HMLNG2MA","created_at":"2026-07-05T01:11:36Z"},{"alias_kind":"pith_short_16","alias_value":"ZU67HMLNG2MAILDC","created_at":"2026-07-05T01:11:36Z"},{"alias_kind":"pith_short_8","alias_value":"ZU67HMLN","created_at":"2026-07-05T01:11:36Z"}],"graph_snapshots":[{"event_id":"sha256:baea1fef61078dd3b842ae11460265f23f720134194ce48ba8666b362082a65e","target":"graph","created_at":"2026-07-05T01:11:36Z","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/2006.11182/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we study the $\\lambda$-regularized $A$-optimal design problem and introduce the $\\lambda$-regularized proportional volume sampling algorithm, generalized from [Nikolov, Singh, and Tantipongpipat, 2019], for this problem with the approximation guarantee that extends upon the previous work. In this problem, we are given vectors $v_1,\\ldots,v_n\\in\\mathbb{R}^d$ in $d$ dimensions, a budget $k\\leq n$, and the regularizer parameter $\\lambda\\geq0$, and the goal is to find a subset $S\\subseteq [n]$ of size $k$ that minimizes the trace of $\\left(\\sum_{i\\in S}v_iv_i^\\top + \\lambda I_d\\right","authors_text":"Uthaipon Tantipongpipat","cross_cats":["cs.LG","math.OC","stat.CO","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2020-06-19T15:17:57Z","title":"$\\lambda$-Regularized A-Optimal Design and its Approximation by $\\lambda$-Regularized Proportional Volume Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.11182","kind":"arxiv","version":1},"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":"sha256:79013cd424341c4c54552d046cee802b7cc917a6aed8a85be27fa90fa4d20021","target":"record","created_at":"2026-07-05T01:11:36Z","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":"490a815747320dec54b349c0876c6ff19466c640de697dddcbdd2f9991a8b1fd","cross_cats_sorted":["cs.LG","math.OC","stat.CO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2020-06-19T15:17:57Z","title_canon_sha256":"ac0bb0d44300d62486cb69ea7ce55d70da2ac73add94554fa906c894620b80d8"},"schema_version":"1.0","source":{"id":"2006.11182","kind":"arxiv","version":1}},"canonical_sha256":"cd3df3b16d3698042c622ba832ac4457cfd70db88f927eeb97c7a6a70f74929d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cd3df3b16d3698042c622ba832ac4457cfd70db88f927eeb97c7a6a70f74929d","first_computed_at":"2026-07-05T01:11:36.149094Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:11:36.149094Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cVZRajWO4ZFFM6bT9Dfi1PmaNtuvJPa0J/1wZPTRfWNgyxZhpuXlhGGoy5D3HEtQqcWw3D54QmGelNuD+ahHBw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:11:36.149494Z","signed_message":"canonical_sha256_bytes"},"source_id":"2006.11182","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79013cd424341c4c54552d046cee802b7cc917a6aed8a85be27fa90fa4d20021","sha256:baea1fef61078dd3b842ae11460265f23f720134194ce48ba8666b362082a65e"],"state_sha256":"c727ff8c17f8a1af89dc07f28292318145171fac5fd176dd3fc0608ace5a3dba"}