{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:AXH2YCEKUWXHJPHJBFDW2VVASZ","short_pith_number":"pith:AXH2YCEK","canonical_record":{"source":{"id":"1706.02412","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-07T23:35:13Z","cross_cats_sorted":[],"title_canon_sha256":"6160d8f557909440c08c16a2b5fa363b8e270e5a78958b688b5348e52bed638e","abstract_canon_sha256":"a5ae0c896c8927faf1c45937038197d73f36eaf96cc4106b643779770d4069e4"},"schema_version":"1.0"},"canonical_sha256":"05cfac088aa5ae74bce909476d56a0967fb62cc96ead5040fb6af49606b8aad1","source":{"kind":"arxiv","id":"1706.02412","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02412","created_at":"2026-05-18T00:16:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02412v2","created_at":"2026-05-18T00:16:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02412","created_at":"2026-05-18T00:16:14Z"},{"alias_kind":"pith_short_12","alias_value":"AXH2YCEKUWXH","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"AXH2YCEKUWXHJPHJ","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"AXH2YCEK","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:AXH2YCEKUWXHJPHJBFDW2VVASZ","target":"record","payload":{"canonical_record":{"source":{"id":"1706.02412","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-07T23:35:13Z","cross_cats_sorted":[],"title_canon_sha256":"6160d8f557909440c08c16a2b5fa363b8e270e5a78958b688b5348e52bed638e","abstract_canon_sha256":"a5ae0c896c8927faf1c45937038197d73f36eaf96cc4106b643779770d4069e4"},"schema_version":"1.0"},"canonical_sha256":"05cfac088aa5ae74bce909476d56a0967fb62cc96ead5040fb6af49606b8aad1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:14.562817Z","signature_b64":"y+iAWLpbsAqeJLdyLGPwlSXsGCy3XXbI6NukFaMUDN5GbkHeRomITjPHCTOsyNvSywfRv2nXU2uXBzTuY8tdDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05cfac088aa5ae74bce909476d56a0967fb62cc96ead5040fb6af49606b8aad1","last_reissued_at":"2026-05-18T00:16:14.561946Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:14.561946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.02412","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z57+JpqgG7sKHwTNAgiFzXaPvgN71LEO+W4tG0gHNs0ZBD2pHF3Xlhru5dLzt7hxovS+XZRbtpKkjfeHb42PDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:04:34.943835Z"},"content_sha256":"148f788fbefe958e78e4fc7a7ff5e97352e53629ed934f5057d69d6417714dfe","schema_version":"1.0","event_id":"sha256:148f788fbefe958e78e4fc7a7ff5e97352e53629ed934f5057d69d6417714dfe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:AXH2YCEKUWXHJPHJBFDW2VVASZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Ioannis Ch. Paschalidis, Ruidi Chen","submitted_at":"2017-06-07T23:35:13Z","abstract_excerpt":"We present a Distributionally Robust Optimization (DRO) approach to estimate a robustified regression plane in a linear regression setting, when the observed samples are potentially contaminated with adversarially corrupted outliers. Our approach mitigates the impact of outliers through hedging against a family of distributions on the observed data, some of which assign very low probabilities to the outliers. The set of distributions under consideration are close to the empirical distribution in the sense of the Wasserstein metric. We show that this DRO formulation can be relaxed to a convex o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02412","kind":"arxiv","version":2},"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":"2026-05-18T00:16:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/+mXIQ5qHgNcTS/fiSUN5MF7PnPWeNkXZBJVFkoR3L0+sFhn7yYubIakw4NXxBSPx1QoVuQtf2A8qAvp4tAADA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T21:04:34.944277Z"},"content_sha256":"b7fc126216e4a01b2b393ed057d52ee7886b4245ea96de73ea1cddb2caec3dce","schema_version":"1.0","event_id":"sha256:b7fc126216e4a01b2b393ed057d52ee7886b4245ea96de73ea1cddb2caec3dce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AXH2YCEKUWXHJPHJBFDW2VVASZ/bundle.json","state_url":"https://pith.science/pith/AXH2YCEKUWXHJPHJBFDW2VVASZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AXH2YCEKUWXHJPHJBFDW2VVASZ/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":"2026-05-31T21:04:34Z","links":{"resolver":"https://pith.science/pith/AXH2YCEKUWXHJPHJBFDW2VVASZ","bundle":"https://pith.science/pith/AXH2YCEKUWXHJPHJBFDW2VVASZ/bundle.json","state":"https://pith.science/pith/AXH2YCEKUWXHJPHJBFDW2VVASZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AXH2YCEKUWXHJPHJBFDW2VVASZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:AXH2YCEKUWXHJPHJBFDW2VVASZ","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":"a5ae0c896c8927faf1c45937038197d73f36eaf96cc4106b643779770d4069e4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-07T23:35:13Z","title_canon_sha256":"6160d8f557909440c08c16a2b5fa363b8e270e5a78958b688b5348e52bed638e"},"schema_version":"1.0","source":{"id":"1706.02412","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02412","created_at":"2026-05-18T00:16:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02412v2","created_at":"2026-05-18T00:16:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02412","created_at":"2026-05-18T00:16:14Z"},{"alias_kind":"pith_short_12","alias_value":"AXH2YCEKUWXH","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"AXH2YCEKUWXHJPHJ","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"AXH2YCEK","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:b7fc126216e4a01b2b393ed057d52ee7886b4245ea96de73ea1cddb2caec3dce","target":"graph","created_at":"2026-05-18T00:16:14Z","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 present a Distributionally Robust Optimization (DRO) approach to estimate a robustified regression plane in a linear regression setting, when the observed samples are potentially contaminated with adversarially corrupted outliers. Our approach mitigates the impact of outliers through hedging against a family of distributions on the observed data, some of which assign very low probabilities to the outliers. The set of distributions under consideration are close to the empirical distribution in the sense of the Wasserstein metric. We show that this DRO formulation can be relaxed to a convex o","authors_text":"Ioannis Ch. Paschalidis, Ruidi Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-07T23:35:13Z","title":"A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02412","kind":"arxiv","version":2},"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":"sha256:148f788fbefe958e78e4fc7a7ff5e97352e53629ed934f5057d69d6417714dfe","target":"record","created_at":"2026-05-18T00:16:14Z","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":"a5ae0c896c8927faf1c45937038197d73f36eaf96cc4106b643779770d4069e4","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-07T23:35:13Z","title_canon_sha256":"6160d8f557909440c08c16a2b5fa363b8e270e5a78958b688b5348e52bed638e"},"schema_version":"1.0","source":{"id":"1706.02412","kind":"arxiv","version":2}},"canonical_sha256":"05cfac088aa5ae74bce909476d56a0967fb62cc96ead5040fb6af49606b8aad1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"05cfac088aa5ae74bce909476d56a0967fb62cc96ead5040fb6af49606b8aad1","first_computed_at":"2026-05-18T00:16:14.561946Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:14.561946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y+iAWLpbsAqeJLdyLGPwlSXsGCy3XXbI6NukFaMUDN5GbkHeRomITjPHCTOsyNvSywfRv2nXU2uXBzTuY8tdDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:14.562817Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.02412","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:148f788fbefe958e78e4fc7a7ff5e97352e53629ed934f5057d69d6417714dfe","sha256:b7fc126216e4a01b2b393ed057d52ee7886b4245ea96de73ea1cddb2caec3dce"],"state_sha256":"a42dd0eafec411f929765d2516bad98c2a6685e937fa34e14f193b2e5b2c1d2e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BovwB8pQncInDmkhoedUkGB7S5tnOA6eHfY+qh8yd0ISmhsx9q6daDWNUOXb36KmK6TBFa7cQmwv8fIU7tkkAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T21:04:34.949964Z","bundle_sha256":"2825a6c7d8dc51f31695021096ffa9b1e686596c6e332917dcda5cc7b5a362e2"}}