{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:R4DSVE6FEPTDA6PXMKGMTZAWAB","short_pith_number":"pith:R4DSVE6F","canonical_record":{"source":{"id":"1711.08113","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-22T02:39:21Z","cross_cats_sorted":[],"title_canon_sha256":"3d8307f571e4bc31260f8ce31d452327ba7583d5d9400110289f2a32579b2977","abstract_canon_sha256":"35115cf2d4806e454b32a3d498e9196f855471401a34f4a67c7513a869feb1a5"},"schema_version":"1.0"},"canonical_sha256":"8f072a93c523e63079f7628cc9e416007f0f3c03c49a0968febffdc2a0481042","source":{"kind":"arxiv","id":"1711.08113","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08113","created_at":"2026-05-18T00:29:50Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08113v1","created_at":"2026-05-18T00:29:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08113","created_at":"2026-05-18T00:29:50Z"},{"alias_kind":"pith_short_12","alias_value":"R4DSVE6FEPTD","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"R4DSVE6FEPTDA6PX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"R4DSVE6F","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:R4DSVE6FEPTDA6PXMKGMTZAWAB","target":"record","payload":{"canonical_record":{"source":{"id":"1711.08113","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-22T02:39:21Z","cross_cats_sorted":[],"title_canon_sha256":"3d8307f571e4bc31260f8ce31d452327ba7583d5d9400110289f2a32579b2977","abstract_canon_sha256":"35115cf2d4806e454b32a3d498e9196f855471401a34f4a67c7513a869feb1a5"},"schema_version":"1.0"},"canonical_sha256":"8f072a93c523e63079f7628cc9e416007f0f3c03c49a0968febffdc2a0481042","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:50.650329Z","signature_b64":"izIZ8hN3F/utqhdYA2Pzz3myFJDJmoaQ8oMAKuYTblViNqfWYMUkwaiuiUXWAgXAFctaq+F/dxyoYlDTJ0fRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f072a93c523e63079f7628cc9e416007f0f3c03c49a0968febffdc2a0481042","last_reissued_at":"2026-05-18T00:29:50.649840Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:50.649840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.08113","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":"2026-05-18T00:29:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gzrp0vxIy5Wg09gMRSh85VSK4gxSKXBvWI6tkXKM8ePSUVEprvkmkJSy7Tejs2qnx29dXu+0qTUDUakVPtgSDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T06:59:07.403722Z"},"content_sha256":"21eba30464149887e49086f5d849728cd5f1e6efa1a4502a328a14fe9b57d465","schema_version":"1.0","event_id":"sha256:21eba30464149887e49086f5d849728cd5f1e6efa1a4502a328a14fe9b57d465"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:R4DSVE6FEPTDA6PXMKGMTZAWAB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Discrete Distributions from Untrusted Batches","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gregory Valiant, Mingda Qiao","submitted_at":"2017-11-22T02:39:21Z","abstract_excerpt":"We consider the problem of learning a discrete distribution in the presence of an $\\epsilon$ fraction of malicious data sources. Specifically, we consider the setting where there is some underlying distribution, $p$, and each data source provides a batch of $\\ge k$ samples, with the guarantee that at least a $(1-\\epsilon)$ fraction of the sources draw their samples from a distribution with total variation distance at most $\\eta$ from $p$. We make no assumptions on the data provided by the remaining $\\epsilon$ fraction of sources--this data can even be chosen as an adversarial function of the $"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08113","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:29:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pn0oeh3SnrjpIUAK/vOxUWq2f4tfaCFuZmcdQb5FqRHL3dYzhOLieBgywi7JOYaqj7ABcHwi48Qf4KQmhhBgAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T06:59:07.404062Z"},"content_sha256":"93f81d0b9535ebb514328c7cab56ac761e17ccd951e41f60a2a968cf8e1256fd","schema_version":"1.0","event_id":"sha256:93f81d0b9535ebb514328c7cab56ac761e17ccd951e41f60a2a968cf8e1256fd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R4DSVE6FEPTDA6PXMKGMTZAWAB/bundle.json","state_url":"https://pith.science/pith/R4DSVE6FEPTDA6PXMKGMTZAWAB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R4DSVE6FEPTDA6PXMKGMTZAWAB/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":"2026-08-21T06:59:07Z","links":{"resolver":"https://pith.science/pith/R4DSVE6FEPTDA6PXMKGMTZAWAB","bundle":"https://pith.science/pith/R4DSVE6FEPTDA6PXMKGMTZAWAB/bundle.json","state":"https://pith.science/pith/R4DSVE6FEPTDA6PXMKGMTZAWAB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R4DSVE6FEPTDA6PXMKGMTZAWAB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:R4DSVE6FEPTDA6PXMKGMTZAWAB","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":"35115cf2d4806e454b32a3d498e9196f855471401a34f4a67c7513a869feb1a5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-22T02:39:21Z","title_canon_sha256":"3d8307f571e4bc31260f8ce31d452327ba7583d5d9400110289f2a32579b2977"},"schema_version":"1.0","source":{"id":"1711.08113","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08113","created_at":"2026-05-18T00:29:50Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08113v1","created_at":"2026-05-18T00:29:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08113","created_at":"2026-05-18T00:29:50Z"},{"alias_kind":"pith_short_12","alias_value":"R4DSVE6FEPTD","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"R4DSVE6FEPTDA6PX","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"R4DSVE6F","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:93f81d0b9535ebb514328c7cab56ac761e17ccd951e41f60a2a968cf8e1256fd","target":"graph","created_at":"2026-05-18T00:29:50Z","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"},"paper":{"abstract_excerpt":"We consider the problem of learning a discrete distribution in the presence of an $\\epsilon$ fraction of malicious data sources. Specifically, we consider the setting where there is some underlying distribution, $p$, and each data source provides a batch of $\\ge k$ samples, with the guarantee that at least a $(1-\\epsilon)$ fraction of the sources draw their samples from a distribution with total variation distance at most $\\eta$ from $p$. We make no assumptions on the data provided by the remaining $\\epsilon$ fraction of sources--this data can even be chosen as an adversarial function of the $","authors_text":"Gregory Valiant, Mingda Qiao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-22T02:39:21Z","title":"Learning Discrete Distributions from Untrusted Batches"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08113","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":"sha256:21eba30464149887e49086f5d849728cd5f1e6efa1a4502a328a14fe9b57d465","target":"record","created_at":"2026-05-18T00:29:50Z","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":"35115cf2d4806e454b32a3d498e9196f855471401a34f4a67c7513a869feb1a5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-11-22T02:39:21Z","title_canon_sha256":"3d8307f571e4bc31260f8ce31d452327ba7583d5d9400110289f2a32579b2977"},"schema_version":"1.0","source":{"id":"1711.08113","kind":"arxiv","version":1}},"canonical_sha256":"8f072a93c523e63079f7628cc9e416007f0f3c03c49a0968febffdc2a0481042","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8f072a93c523e63079f7628cc9e416007f0f3c03c49a0968febffdc2a0481042","first_computed_at":"2026-05-18T00:29:50.649840Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:50.649840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"izIZ8hN3F/utqhdYA2Pzz3myFJDJmoaQ8oMAKuYTblViNqfWYMUkwaiuiUXWAgXAFctaq+F/dxyoYlDTJ0fRBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:50.650329Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.08113","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21eba30464149887e49086f5d849728cd5f1e6efa1a4502a328a14fe9b57d465","sha256:93f81d0b9535ebb514328c7cab56ac761e17ccd951e41f60a2a968cf8e1256fd"],"state_sha256":"52a2610ef027c0e92f88f75877e6a28c77d78fad8d97be15771059dfb5a53d67"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"awWyOSsnGQ0g2r2Iv9tG7Y5tMUuug4hAtB0beF32CGYier0aooOvGpMWj9KxBI/aeI50DHA24zev/sIjhEDCBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T06:59:07.406348Z","bundle_sha256":"cf299140636cdd4de95ee71a4be6768dfd0e7e306d40b534768d610723562f75"}}