{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:W6DVUILBVAO64ETFBGDIDNQERX","short_pith_number":"pith:W6DVUILB","schema_version":"1.0","canonical_sha256":"b7875a2161a81dee1265098681b6048dd6e7ca8d205295ba63af3f4b9762c2a6","source":{"kind":"arxiv","id":"1702.07709","version":1},"attestation_state":"computed","paper":{"title":"Computationally Efficient Robust Estimation of Sparse Functionals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"stat.ML","authors_text":"Aarti Singh, Simon S. Du, Sivaraman Balakrishnan","submitted_at":"2017-02-24T18:59:08Z","abstract_excerpt":"Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions. This problem is exacerbated in modern high-dimensional settings, where the problem dimension can grow with and possibly exceed the sample size. We consider the problem of robust estimation of sparse functionals, and provide a computationally and statistically efficient algorithm in the high-dimensional setting. Our theory identifies a unified set of deterministic conditions under which our algorithm guarantees accurate recovery. By further establishing that these determinis"},"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":"1702.07709","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-24T18:59:08Z","cross_cats_sorted":["cs.DS","cs.LG"],"title_canon_sha256":"cfa266e6d4faac54936c2b36c7487fa2a5d88a21e6ea2fa432fbd8b25338fe7a","abstract_canon_sha256":"8788886c0886cbafbcefd5adc687622fafa365f3111e168af9542b13be0e65cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:02.444884Z","signature_b64":"dR3/xJeV2Pr9A94MhuQxrm+7q3JlT+y60UJ7FV91RZP8X8Azf8e0VmaApoWGb08ccBF6w1EQ0Grlk4EtRekGAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7875a2161a81dee1265098681b6048dd6e7ca8d205295ba63af3f4b9762c2a6","last_reissued_at":"2026-05-18T00:50:02.444184Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:02.444184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Computationally Efficient Robust Estimation of Sparse Functionals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","cs.LG"],"primary_cat":"stat.ML","authors_text":"Aarti Singh, Simon S. Du, Sivaraman Balakrishnan","submitted_at":"2017-02-24T18:59:08Z","abstract_excerpt":"Many conventional statistical procedures are extremely sensitive to seemingly minor deviations from modeling assumptions. This problem is exacerbated in modern high-dimensional settings, where the problem dimension can grow with and possibly exceed the sample size. We consider the problem of robust estimation of sparse functionals, and provide a computationally and statistically efficient algorithm in the high-dimensional setting. Our theory identifies a unified set of deterministic conditions under which our algorithm guarantees accurate recovery. By further establishing that these determinis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.07709","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":"1702.07709","created_at":"2026-05-18T00:50:02.444289+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.07709v1","created_at":"2026-05-18T00:50:02.444289+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.07709","created_at":"2026-05-18T00:50:02.444289+00:00"},{"alias_kind":"pith_short_12","alias_value":"W6DVUILBVAO6","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_16","alias_value":"W6DVUILBVAO64ETF","created_at":"2026-05-18T12:31:53.515858+00:00"},{"alias_kind":"pith_short_8","alias_value":"W6DVUILB","created_at":"2026-05-18T12:31:53.515858+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/W6DVUILBVAO64ETFBGDIDNQERX","json":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX.json","graph_json":"https://pith.science/api/pith-number/W6DVUILBVAO64ETFBGDIDNQERX/graph.json","events_json":"https://pith.science/api/pith-number/W6DVUILBVAO64ETFBGDIDNQERX/events.json","paper":"https://pith.science/paper/W6DVUILB"},"agent_actions":{"view_html":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX","download_json":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX.json","view_paper":"https://pith.science/paper/W6DVUILB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.07709&json=true","fetch_graph":"https://pith.science/api/pith-number/W6DVUILBVAO64ETFBGDIDNQERX/graph.json","fetch_events":"https://pith.science/api/pith-number/W6DVUILBVAO64ETFBGDIDNQERX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX/action/storage_attestation","attest_author":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX/action/author_attestation","sign_citation":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX/action/citation_signature","submit_replication":"https://pith.science/pith/W6DVUILBVAO64ETFBGDIDNQERX/action/replication_record"}},"created_at":"2026-05-18T00:50:02.444289+00:00","updated_at":"2026-05-18T00:50:02.444289+00:00"}