{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:7GUDXRB5K2764NFV6CMCNCSGWK","short_pith_number":"pith:7GUDXRB5","schema_version":"1.0","canonical_sha256":"f9a83bc43d56bfee34b5f098268a46b2a534e49749bbcd60e273e359d59b5247","source":{"kind":"arxiv","id":"1612.08263","version":3},"attestation_state":"computed","paper":{"title":"Decentralized RLS with Data-Adaptive Censoring for Regressions over Large-Scale Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","math.OC"],"primary_cat":"cs.SY","authors_text":"Dimitris Berberidis, Georgios B. Giannakis, Qing Ling, Zheng Yu, Zifeng Wang","submitted_at":"2016-12-25T11:38:27Z","abstract_excerpt":"The deluge of networked data motivates the development of algorithms for computation- and communication-efficient information processing. In this context, three data-adaptive censoring strategies are introduced to considerably reduce the computation and communication overhead of decentralized recursive least-squares (D-RLS) solvers. The first relies on alternating minimization and the stochastic Newton iteration to minimize a network-wide cost, which discards observations with small innovations. In the resultant algorithm, each node performs local data-adaptive censoring to reduce computations"},"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":"1612.08263","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2016-12-25T11:38:27Z","cross_cats_sorted":["cs.NI","math.OC"],"title_canon_sha256":"9e61811df2d7ff9fe5b9184e4d37a09e1c6868674d6f581577ccb3342cdd00ef","abstract_canon_sha256":"cb6421b58f1427d6062f2b72a2334dd1e1f275190b38a431136432e2efd26672"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:08.419902Z","signature_b64":"bdyyoiDD9Ipvi51JZgq/u0ZWBPvsh5vhad3Qoyyc2B7yrPeR3Dbo/IpylvRTuvCd6srO2JYwj2Q/sjxJzPxxCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9a83bc43d56bfee34b5f098268a46b2a534e49749bbcd60e273e359d59b5247","last_reissued_at":"2026-05-18T00:26:08.419216Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:08.419216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decentralized RLS with Data-Adaptive Censoring for Regressions over Large-Scale Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI","math.OC"],"primary_cat":"cs.SY","authors_text":"Dimitris Berberidis, Georgios B. Giannakis, Qing Ling, Zheng Yu, Zifeng Wang","submitted_at":"2016-12-25T11:38:27Z","abstract_excerpt":"The deluge of networked data motivates the development of algorithms for computation- and communication-efficient information processing. In this context, three data-adaptive censoring strategies are introduced to considerably reduce the computation and communication overhead of decentralized recursive least-squares (D-RLS) solvers. The first relies on alternating minimization and the stochastic Newton iteration to minimize a network-wide cost, which discards observations with small innovations. In the resultant algorithm, each node performs local data-adaptive censoring to reduce computations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.08263","kind":"arxiv","version":3},"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":"1612.08263","created_at":"2026-05-18T00:26:08.419330+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.08263v3","created_at":"2026-05-18T00:26:08.419330+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.08263","created_at":"2026-05-18T00:26:08.419330+00:00"},{"alias_kind":"pith_short_12","alias_value":"7GUDXRB5K276","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_16","alias_value":"7GUDXRB5K2764NFV","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_8","alias_value":"7GUDXRB5","created_at":"2026-05-18T12:30:04.600751+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/7GUDXRB5K2764NFV6CMCNCSGWK","json":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK.json","graph_json":"https://pith.science/api/pith-number/7GUDXRB5K2764NFV6CMCNCSGWK/graph.json","events_json":"https://pith.science/api/pith-number/7GUDXRB5K2764NFV6CMCNCSGWK/events.json","paper":"https://pith.science/paper/7GUDXRB5"},"agent_actions":{"view_html":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK","download_json":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK.json","view_paper":"https://pith.science/paper/7GUDXRB5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.08263&json=true","fetch_graph":"https://pith.science/api/pith-number/7GUDXRB5K2764NFV6CMCNCSGWK/graph.json","fetch_events":"https://pith.science/api/pith-number/7GUDXRB5K2764NFV6CMCNCSGWK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK/action/storage_attestation","attest_author":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK/action/author_attestation","sign_citation":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK/action/citation_signature","submit_replication":"https://pith.science/pith/7GUDXRB5K2764NFV6CMCNCSGWK/action/replication_record"}},"created_at":"2026-05-18T00:26:08.419330+00:00","updated_at":"2026-05-18T00:26:08.419330+00:00"}