{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3MAO75GE2W6SE5AHTOMZLKERKL","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":"ec429b8def266f0599b1a187f44689fc08d1b0f2e6e42cfd8c6ffd34449d0265","cross_cats_sorted":["cs.CR","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T20:33:26Z","title_canon_sha256":"d32e802be6179a6f258f06092ef5e7a2e153ded65d2a5def5695fdfe82956095"},"schema_version":"1.0","source":{"id":"2301.13273","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.13273","created_at":"2026-07-05T05:37:13Z"},{"alias_kind":"arxiv_version","alias_value":"2301.13273v1","created_at":"2026-07-05T05:37:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.13273","created_at":"2026-07-05T05:37:13Z"},{"alias_kind":"pith_short_12","alias_value":"3MAO75GE2W6S","created_at":"2026-07-05T05:37:13Z"},{"alias_kind":"pith_short_16","alias_value":"3MAO75GE2W6SE5AH","created_at":"2026-07-05T05:37:13Z"},{"alias_kind":"pith_short_8","alias_value":"3MAO75GE","created_at":"2026-07-05T05:37:13Z"}],"graph_snapshots":[{"event_id":"sha256:31cb8cdb8cf5b63a3dc09c9dcd211e74dfd84c2233940519b2df648842a5ef27","target":"graph","created_at":"2026-07-05T05:37:13Z","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/2301.13273/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the canonical statistical estimation problem of linear regression from $n$ i.i.d.~examples under $(\\varepsilon,\\delta)$-differential privacy when some response variables are adversarially corrupted. We propose a variant of the popular differentially private stochastic gradient descent (DP-SGD) algorithm with two innovations: a full-batch gradient descent to improve sample complexity and a novel adaptive clipping to guarantee robustness. When there is no adversarial corruption, this algorithm improves upon the existing state-of-the-art approach and achieves a near optimal sample comple","authors_text":"Arun Sai Suggala, Prateek Jain, Sewoong Oh, Weihao Kong, Xiyang Liu","cross_cats":["cs.CR","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T20:33:26Z","title":"Near Optimal Private and Robust Linear Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.13273","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:bf6612ca38b0cc0ba2f4fc3c5ce1522a81c8c6d67851bd957de9101d2469959f","target":"record","created_at":"2026-07-05T05:37:13Z","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":"ec429b8def266f0599b1a187f44689fc08d1b0f2e6e42cfd8c6ffd34449d0265","cross_cats_sorted":["cs.CR","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-30T20:33:26Z","title_canon_sha256":"d32e802be6179a6f258f06092ef5e7a2e153ded65d2a5def5695fdfe82956095"},"schema_version":"1.0","source":{"id":"2301.13273","kind":"arxiv","version":1}},"canonical_sha256":"db00eff4c4d5bd2274079b9995a89152ff9b035cce78d67566e632a0539fed88","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db00eff4c4d5bd2274079b9995a89152ff9b035cce78d67566e632a0539fed88","first_computed_at":"2026-07-05T05:37:13.113049Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:37:13.113049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uaIPPYpSu0IO7OaEl8IF09HyMexAEh7Uc77yGOVCUvfz8uFMWpVTNJP2QjaYX54vBHDp18g0M1+an/kyaZfjBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:37:13.113535Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.13273","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bf6612ca38b0cc0ba2f4fc3c5ce1522a81c8c6d67851bd957de9101d2469959f","sha256:31cb8cdb8cf5b63a3dc09c9dcd211e74dfd84c2233940519b2df648842a5ef27"],"state_sha256":"45f1dda4b3088dc82fbfaa2a7f30030ccfca46ae3960a2acf64040ae4ff0f77c"}