{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:2YRWB6UBSFD7ZWMBJUZRS5NGAE","short_pith_number":"pith:2YRWB6UB","schema_version":"1.0","canonical_sha256":"d62360fa819147fcd9814d331975a6010729d27fff00e31ed3fb362ba796ceb5","source":{"kind":"arxiv","id":"1907.00927","version":1},"attestation_state":"computed","paper":{"title":"A Unified Approach to Robust Mean Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Adarsh Prasad, Pradeep Ravikumar, Sivaraman Balakrishnan","submitted_at":"2019-07-01T17:03:11Z","abstract_excerpt":"In this paper, we develop connections between two seemingly disparate, but central, models in robust statistics: Huber's epsilon-contamination model and the heavy-tailed noise model. We provide conditions under which this connection provides near-statistically-optimal estimators. Building on this connection, we provide a simple variant of recent computationally-efficient algorithms for mean estimation in Huber's model, which given our connection entails that the same efficient sample-pruning based estimators is simultaneously robust to heavy-tailed noise and Huber contamination. Furthermore, w"},"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":"1907.00927","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-07-01T17:03:11Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"132fd29ebefb0948864890807a727746658363bca28d2ed6cce070f4033987c0","abstract_canon_sha256":"08b656413200b079807072e18413886dacf2f972c620618ae5dafa1da9831d16"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:41.545869Z","signature_b64":"3H0VZbDOUmzmR+aYCsjsyr9JzaDxyBBb3/r8aZTet1Aio8Z0Ft8CPRTnMptui3cFWdyDhjgtAq6Dl/ii1AJ4Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d62360fa819147fcd9814d331975a6010729d27fff00e31ed3fb362ba796ceb5","last_reissued_at":"2026-05-17T23:41:41.545392Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:41.545392Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Unified Approach to Robust Mean Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Adarsh Prasad, Pradeep Ravikumar, Sivaraman Balakrishnan","submitted_at":"2019-07-01T17:03:11Z","abstract_excerpt":"In this paper, we develop connections between two seemingly disparate, but central, models in robust statistics: Huber's epsilon-contamination model and the heavy-tailed noise model. We provide conditions under which this connection provides near-statistically-optimal estimators. Building on this connection, we provide a simple variant of recent computationally-efficient algorithms for mean estimation in Huber's model, which given our connection entails that the same efficient sample-pruning based estimators is simultaneously robust to heavy-tailed noise and Huber contamination. Furthermore, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.00927","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":"1907.00927","created_at":"2026-05-17T23:41:41.545460+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.00927v1","created_at":"2026-05-17T23:41:41.545460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.00927","created_at":"2026-05-17T23:41:41.545460+00:00"},{"alias_kind":"pith_short_12","alias_value":"2YRWB6UBSFD7","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"2YRWB6UBSFD7ZWMB","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"2YRWB6UB","created_at":"2026-05-18T12:33:07.085635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.03087","citing_title":"Estimating location parameters in entangled single-sample distributions","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE","json":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE.json","graph_json":"https://pith.science/api/pith-number/2YRWB6UBSFD7ZWMBJUZRS5NGAE/graph.json","events_json":"https://pith.science/api/pith-number/2YRWB6UBSFD7ZWMBJUZRS5NGAE/events.json","paper":"https://pith.science/paper/2YRWB6UB"},"agent_actions":{"view_html":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE","download_json":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE.json","view_paper":"https://pith.science/paper/2YRWB6UB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.00927&json=true","fetch_graph":"https://pith.science/api/pith-number/2YRWB6UBSFD7ZWMBJUZRS5NGAE/graph.json","fetch_events":"https://pith.science/api/pith-number/2YRWB6UBSFD7ZWMBJUZRS5NGAE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE/action/storage_attestation","attest_author":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE/action/author_attestation","sign_citation":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE/action/citation_signature","submit_replication":"https://pith.science/pith/2YRWB6UBSFD7ZWMBJUZRS5NGAE/action/replication_record"}},"created_at":"2026-05-17T23:41:41.545460+00:00","updated_at":"2026-05-17T23:41:41.545460+00:00"}