{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:ZLIBQEPTY344SK6CVQZ2EG7ERK","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":"25e52f1655dd14e3a6375443967432ead7110bc5f13fc86882cf80d63a14a6e0","cross_cats_sorted":["cs.DC","cs.IT","cs.LG","math.IT","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2020-01-24T04:19:47Z","title_canon_sha256":"7405cc1f8a2f4ebb151a43745de9328590fca852d20998aee2806471b9826c51"},"schema_version":"1.0","source":{"id":"2001.08877","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.08877","created_at":"2026-07-05T00:39:16Z"},{"alias_kind":"arxiv_version","alias_value":"2001.08877v1","created_at":"2026-07-05T00:39:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.08877","created_at":"2026-07-05T00:39:16Z"},{"alias_kind":"pith_short_12","alias_value":"ZLIBQEPTY344","created_at":"2026-07-05T00:39:16Z"},{"alias_kind":"pith_short_16","alias_value":"ZLIBQEPTY344SK6C","created_at":"2026-07-05T00:39:16Z"},{"alias_kind":"pith_short_8","alias_value":"ZLIBQEPT","created_at":"2026-07-05T00:39:16Z"}],"graph_snapshots":[{"event_id":"sha256:bec4dcdc36f63d02c77d989bb665b357daed2d9737c1a9033ce786d326d56831","target":"graph","created_at":"2026-07-05T00:39:16Z","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/2001.08877/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study distributed estimation of a Gaussian mean under communication constraints in a decision theoretical framework. Minimax rates of convergence, which characterize the tradeoff between the communication costs and statistical accuracy, are established in both the univariate and multivariate settings. Communication-efficient and statistically optimal procedures are developed. In the univariate case, the optimal rate depends only on the total communication budget, so long as each local machine has at least one bit. However, in the multivariate case, the minimax rate depends on the specific a","authors_text":"Hongji Wei, T. Tony Cai","cross_cats":["cs.DC","cs.IT","cs.LG","math.IT","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2020-01-24T04:19:47Z","title":"Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.08877","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":"sha256:791cae124ac19082c9c7de5a1e7862052ed44c6fbd52345a6e60b286d6708363","target":"record","created_at":"2026-07-05T00:39:16Z","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":"25e52f1655dd14e3a6375443967432ead7110bc5f13fc86882cf80d63a14a6e0","cross_cats_sorted":["cs.DC","cs.IT","cs.LG","math.IT","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2020-01-24T04:19:47Z","title_canon_sha256":"7405cc1f8a2f4ebb151a43745de9328590fca852d20998aee2806471b9826c51"},"schema_version":"1.0","source":{"id":"2001.08877","kind":"arxiv","version":1}},"canonical_sha256":"cad01811f3c6f9c92bc2ac33a21be48aba00baf15687e17d7064638b9be9e532","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cad01811f3c6f9c92bc2ac33a21be48aba00baf15687e17d7064638b9be9e532","first_computed_at":"2026-07-05T00:39:16.670082Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:39:16.670082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1K1P5BiM+eq6Tqhg5I7scw2hxoEjZhUOZp4Ut0uSDAG7a/t4BWVZMHCZlrU3U5IGNC4E+pr/0MO+01vugc/YAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:39:16.670489Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.08877","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:791cae124ac19082c9c7de5a1e7862052ed44c6fbd52345a6e60b286d6708363","sha256:bec4dcdc36f63d02c77d989bb665b357daed2d9737c1a9033ce786d326d56831"],"state_sha256":"a2a6b1f81f061fd550c31be177c1d8013145ef37591d52a3318c770352dfa78c"}