{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:LMPZW2TPONV5V3ORZ4RKYABKLQ","short_pith_number":"pith:LMPZW2TP","schema_version":"1.0","canonical_sha256":"5b1f9b6a6f736bdaedd1cf22ac002a5c0c5c33f89e773a68f60aa8a66e0a5890","source":{"kind":"arxiv","id":"1510.04905","version":1},"attestation_state":"computed","paper":{"title":"Robust Partially-Compressed Least-Squares","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ban Kawas, Karthikeyan N. Ramamurthy, Marek Petrik, Stephen Becker","submitted_at":"2015-10-16T14:59:04Z","abstract_excerpt":"Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off. In this paper, we investigate compressed least-squares problems and propose new models and algorithms that address the issue of error and noise introduced by compression. While maintaining computational efficiency, our models provide robust solutions that are more accurate--relative to solutions of uncom"},"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":"1510.04905","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2015-10-16T14:59:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"120a5011a47c5c886740b4f29f813ec5edcfb48636ca7b99c2ba78fc76b99578","abstract_canon_sha256":"6b58e1062fa0bdc09ba320e9d33c8294a26401ffe3f45e3bd244e54566834768"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:30:00.337364Z","signature_b64":"TV/Pt2LcJPXHsOuv2SzRDKvyo6WLwr4MdtmoK1h0Bc/2Wp7et+0aPXXXzWflCrv2+Kbx0jXPXneaeurp9MJSBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b1f9b6a6f736bdaedd1cf22ac002a5c0c5c33f89e773a68f60aa8a66e0a5890","last_reissued_at":"2026-05-18T01:30:00.336685Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:30:00.336685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Partially-Compressed Least-Squares","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ban Kawas, Karthikeyan N. Ramamurthy, Marek Petrik, Stephen Becker","submitted_at":"2015-10-16T14:59:04Z","abstract_excerpt":"Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efficiently. With a focus on computational efficiency, however, forsaking solutions quality and accuracy becomes the trade-off. In this paper, we investigate compressed least-squares problems and propose new models and algorithms that address the issue of error and noise introduced by compression. While maintaining computational efficiency, our models provide robust solutions that are more accurate--relative to solutions of uncom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.04905","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1510.04905","created_at":"2026-05-18T01:30:00.336785+00:00"},{"alias_kind":"arxiv_version","alias_value":"1510.04905v1","created_at":"2026-05-18T01:30:00.336785+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.04905","created_at":"2026-05-18T01:30:00.336785+00:00"},{"alias_kind":"pith_short_12","alias_value":"LMPZW2TPONV5","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_16","alias_value":"LMPZW2TPONV5V3OR","created_at":"2026-05-18T12:29:29.992203+00:00"},{"alias_kind":"pith_short_8","alias_value":"LMPZW2TP","created_at":"2026-05-18T12:29:29.992203+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/LMPZW2TPONV5V3ORZ4RKYABKLQ","json":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ.json","graph_json":"https://pith.science/api/pith-number/LMPZW2TPONV5V3ORZ4RKYABKLQ/graph.json","events_json":"https://pith.science/api/pith-number/LMPZW2TPONV5V3ORZ4RKYABKLQ/events.json","paper":"https://pith.science/paper/1510.04905"},"agent_actions":{"view_html":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ","download_json":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ.json","view_paper":"https://pith.science/paper/1510.04905","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1510.04905&json=true","fetch_graph":"https://pith.science/api/pith-number/LMPZW2TPONV5V3ORZ4RKYABKLQ/graph.json","fetch_events":"https://pith.science/api/pith-number/LMPZW2TPONV5V3ORZ4RKYABKLQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ/action/storage_attestation","attest_author":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ/action/author_attestation","sign_citation":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ/action/citation_signature","submit_replication":"https://pith.science/pith/LMPZW2TPONV5V3ORZ4RKYABKLQ/action/replication_record"}},"created_at":"2026-05-18T01:30:00.336785+00:00","updated_at":"2026-05-18T01:30:00.336785+00:00"}