{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:HW7YJKGCZJJURLMIBJZLYXXEMY","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":"6a6df085454b9ec408710e9979149070d6d440ef9e62101ace34c7c9ed1390d6","cross_cats_sorted":["cs.CC","cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T16:46:23Z","title_canon_sha256":"bf4ae6e75be6c9a38ba8f1ba5c963f32cc813b8d0560686921ffbb5fa4f851f4"},"schema_version":"1.0","source":{"id":"2302.06512","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.06512","created_at":"2026-07-05T05:41:11Z"},{"alias_kind":"arxiv_version","alias_value":"2302.06512v1","created_at":"2026-07-05T05:41:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.06512","created_at":"2026-07-05T05:41:11Z"},{"alias_kind":"pith_short_12","alias_value":"HW7YJKGCZJJU","created_at":"2026-07-05T05:41:11Z"},{"alias_kind":"pith_short_16","alias_value":"HW7YJKGCZJJURLMI","created_at":"2026-07-05T05:41:11Z"},{"alias_kind":"pith_short_8","alias_value":"HW7YJKGC","created_at":"2026-07-05T05:41:11Z"}],"graph_snapshots":[{"event_id":"sha256:64127e95a0baa8581d40d2d0a2dbaaf99c88481dc424609c9dd0d515930b3c38","target":"graph","created_at":"2026-07-05T05:41:11Z","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/2302.06512/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the task of agnostically learning halfspaces under the Gaussian distribution. Specifically, given labeled examples $(\\mathbf{x},y)$ from an unknown distribution on $\\mathbb{R}^n \\times \\{ \\pm 1\\}$, whose marginal distribution on $\\mathbf{x}$ is the standard Gaussian and the labels $y$ can be arbitrary, the goal is to output a hypothesis with 0-1 loss $\\mathrm{OPT}+\\epsilon$, where $\\mathrm{OPT}$ is the 0-1 loss of the best-fitting halfspace. We prove a near-optimal computational hardness result for this task, under the widely believed sub-exponential time hardness of the Learning with","authors_text":"Daniel M. Kane, Ilias Diakonikolas, Lisheng Ren","cross_cats":["cs.CC","cs.DS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T16:46:23Z","title":"Near-Optimal Cryptographic Hardness of Agnostically Learning Halfspaces and ReLU Regression under Gaussian Marginals"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.06512","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:abb76d146cb4a9c1b71f7e2db390f2f61d3517418228a1bd27df7ccb90bc1731","target":"record","created_at":"2026-07-05T05:41:11Z","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":"6a6df085454b9ec408710e9979149070d6d440ef9e62101ace34c7c9ed1390d6","cross_cats_sorted":["cs.CC","cs.DS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-13T16:46:23Z","title_canon_sha256":"bf4ae6e75be6c9a38ba8f1ba5c963f32cc813b8d0560686921ffbb5fa4f851f4"},"schema_version":"1.0","source":{"id":"2302.06512","kind":"arxiv","version":1}},"canonical_sha256":"3dbf84a8c2ca5348ad880a72bc5ee4660357c6482efff1b57eaa23772f03ac52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3dbf84a8c2ca5348ad880a72bc5ee4660357c6482efff1b57eaa23772f03ac52","first_computed_at":"2026-07-05T05:41:11.817177Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:41:11.817177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xQnfjj++W6Q/33FjPTp3wcve3ktEiYSHta0HIWlQgOMgH9iEUtdBALpr/DPm/xNLI1bFXXSJR+a1V5bCPZcVCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:41:11.817583Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.06512","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:abb76d146cb4a9c1b71f7e2db390f2f61d3517418228a1bd27df7ccb90bc1731","sha256:64127e95a0baa8581d40d2d0a2dbaaf99c88481dc424609c9dd0d515930b3c38"],"state_sha256":"3d6e4bb8be93140839a3d4149355a91bb141fc8e525168e7c058ef0159695f1d"}