{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4SBXIKELYZOQU46I5JFHTGI3WC","short_pith_number":"pith:4SBXIKEL","canonical_record":{"source":{"id":"2503.09802","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-12T20:11:07Z","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"830739d0505675b07c86f04f78a0f268bd85e653227bc9f547824e5fbbd122bf","abstract_canon_sha256":"60c1614a6a452eb412d12a2b2871b2b8f2ea4d028719d83b60975db80c028d84"},"schema_version":"1.0"},"canonical_sha256":"e48374288bc65d0a73c8ea4a79991bb0bd0c6a1776adc840f9205c2b28663997","source":{"kind":"arxiv","id":"2503.09802","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.09802","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"arxiv_version","alias_value":"2503.09802v1","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.09802","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"pith_short_12","alias_value":"4SBXIKELYZOQ","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"pith_short_16","alias_value":"4SBXIKELYZOQU46I","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"pith_short_8","alias_value":"4SBXIKEL","created_at":"2026-07-05T10:30:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4SBXIKELYZOQU46I5JFHTGI3WC","target":"record","payload":{"canonical_record":{"source":{"id":"2503.09802","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-12T20:11:07Z","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"830739d0505675b07c86f04f78a0f268bd85e653227bc9f547824e5fbbd122bf","abstract_canon_sha256":"60c1614a6a452eb412d12a2b2871b2b8f2ea4d028719d83b60975db80c028d84"},"schema_version":"1.0"},"canonical_sha256":"e48374288bc65d0a73c8ea4a79991bb0bd0c6a1776adc840f9205c2b28663997","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:30:08.736827Z","signature_b64":"Mo2qwOFyJFEzI2YS36z0ukiUPhkScJeyB5TTuevKrahtt5P0ffCcHSINWTXjK94K+0k82jQsvhOhdN13fWtmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e48374288bc65d0a73c8ea4a79991bb0bd0c6a1776adc840f9205c2b28663997","last_reissued_at":"2026-07-05T10:30:08.736269Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:30:08.736269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.09802","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:30:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eY/cceKQFe9oVn2ffyfpA25IoZnk7EPF7TfRA85HbpqWAqErUSi5vOB/lazC5SM0dDL1yAj0p3QLoL1MLY2ZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T11:29:55.491915Z"},"content_sha256":"2b506b9deaf92409c3f8417ee08ed2782767994f7920368b201c05196a699350","schema_version":"1.0","event_id":"sha256:2b506b9deaf92409c3f8417ee08ed2782767994f7920368b201c05196a699350"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4SBXIKELYZOQU46I5JFHTGI3WC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Batch List-Decodable Linear Regression via Higher Moments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.LG","authors_text":"Daniel M. Kane, Ilias Diakonikolas, Sihan Liu, Sushrut Karmalkar, Thanasis Pittas","submitted_at":"2025-03-12T20:11:07Z","abstract_excerpt":"We study the task of list-decodable linear regression using batches. A batch is called clean if it consists of i.i.d. samples from an unknown linear regression distribution. For a parameter $\\alpha \\in (0, 1/2)$, an unknown $\\alpha$-fraction of the batches are clean and no assumptions are made on the remaining ones. The goal is to output a small list of vectors at least one of which is close to the true regressor vector in $\\ell_2$-norm. [DJKS23] gave an efficient algorithm, under natural distributional assumptions, with the following guarantee. Assuming that the batch size $n$ satisfies $n \\g"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.09802","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.09802/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:30:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kOH5GVZ83sqK5MKPtF8U3RmWmOTrtKVXTtf6ix4R0RThHd1qxh9Zgc2xKztljLbb4dxvftP6qqQhXfQx0yddDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T11:29:55.492434Z"},"content_sha256":"e55d02563c59154d1e08689373cf11859942eb8c738a853dd3a32431484d4883","schema_version":"1.0","event_id":"sha256:e55d02563c59154d1e08689373cf11859942eb8c738a853dd3a32431484d4883"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4SBXIKELYZOQU46I5JFHTGI3WC/bundle.json","state_url":"https://pith.science/pith/4SBXIKELYZOQU46I5JFHTGI3WC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4SBXIKELYZOQU46I5JFHTGI3WC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-14T11:29:55Z","links":{"resolver":"https://pith.science/pith/4SBXIKELYZOQU46I5JFHTGI3WC","bundle":"https://pith.science/pith/4SBXIKELYZOQU46I5JFHTGI3WC/bundle.json","state":"https://pith.science/pith/4SBXIKELYZOQU46I5JFHTGI3WC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4SBXIKELYZOQU46I5JFHTGI3WC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4SBXIKELYZOQU46I5JFHTGI3WC","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":"60c1614a6a452eb412d12a2b2871b2b8f2ea4d028719d83b60975db80c028d84","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-12T20:11:07Z","title_canon_sha256":"830739d0505675b07c86f04f78a0f268bd85e653227bc9f547824e5fbbd122bf"},"schema_version":"1.0","source":{"id":"2503.09802","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.09802","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"arxiv_version","alias_value":"2503.09802v1","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.09802","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"pith_short_12","alias_value":"4SBXIKELYZOQ","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"pith_short_16","alias_value":"4SBXIKELYZOQU46I","created_at":"2026-07-05T10:30:08Z"},{"alias_kind":"pith_short_8","alias_value":"4SBXIKEL","created_at":"2026-07-05T10:30:08Z"}],"graph_snapshots":[{"event_id":"sha256:e55d02563c59154d1e08689373cf11859942eb8c738a853dd3a32431484d4883","target":"graph","created_at":"2026-07-05T10:30:08Z","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/2503.09802/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the task of list-decodable linear regression using batches. A batch is called clean if it consists of i.i.d. samples from an unknown linear regression distribution. For a parameter $\\alpha \\in (0, 1/2)$, an unknown $\\alpha$-fraction of the batches are clean and no assumptions are made on the remaining ones. The goal is to output a small list of vectors at least one of which is close to the true regressor vector in $\\ell_2$-norm. [DJKS23] gave an efficient algorithm, under natural distributional assumptions, with the following guarantee. Assuming that the batch size $n$ satisfies $n \\g","authors_text":"Daniel M. Kane, Ilias Diakonikolas, Sihan Liu, Sushrut Karmalkar, Thanasis Pittas","cross_cats":["cs.DS","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-12T20:11:07Z","title":"Batch List-Decodable Linear Regression via Higher Moments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.09802","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":"sha256:2b506b9deaf92409c3f8417ee08ed2782767994f7920368b201c05196a699350","target":"record","created_at":"2026-07-05T10:30:08Z","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":"60c1614a6a452eb412d12a2b2871b2b8f2ea4d028719d83b60975db80c028d84","cross_cats_sorted":["cs.DS","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-03-12T20:11:07Z","title_canon_sha256":"830739d0505675b07c86f04f78a0f268bd85e653227bc9f547824e5fbbd122bf"},"schema_version":"1.0","source":{"id":"2503.09802","kind":"arxiv","version":1}},"canonical_sha256":"e48374288bc65d0a73c8ea4a79991bb0bd0c6a1776adc840f9205c2b28663997","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e48374288bc65d0a73c8ea4a79991bb0bd0c6a1776adc840f9205c2b28663997","first_computed_at":"2026-07-05T10:30:08.736269Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:30:08.736269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mo2qwOFyJFEzI2YS36z0ukiUPhkScJeyB5TTuevKrahtt5P0ffCcHSINWTXjK94K+0k82jQsvhOhdN13fWtmCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:30:08.736827Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.09802","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b506b9deaf92409c3f8417ee08ed2782767994f7920368b201c05196a699350","sha256:e55d02563c59154d1e08689373cf11859942eb8c738a853dd3a32431484d4883"],"state_sha256":"cf059c56a2d0d3df25979d4d99c301ce8aaf833667ad04262941db5521e9ffb5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VU2QEYGOG2fUgICAB4E024elQXGWQyIlmkcVeMb5IVMK6ZSmXj+cTJIGT9U3au6vBmhs3ixKhz8vMuoBtX79Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T11:29:55.497421Z","bundle_sha256":"3cfdefb7b7020b07bdf693abb7a732a217bfe3d4b88d4f63d0ee91a183a7a44c"}}