{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:F22T6ZWBDQZQDEMUY33H353BFY","short_pith_number":"pith:F22T6ZWB","canonical_record":{"source":{"id":"2306.04064","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-06T23:24:02Z","cross_cats_sorted":[],"title_canon_sha256":"73cf71d8e8a12e298ab3be4d3e00f787d0cf7287dbb675d94156b4c6ad184a40","abstract_canon_sha256":"b17e16c55d386e03e373e466ced773186c67454690c18c036843ef3f68462bff"},"schema_version":"1.0"},"canonical_sha256":"2eb53f66c11c33019194c6f67df7612e1126ea14d72e623d2d728abe4eacae3d","source":{"kind":"arxiv","id":"2306.04064","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04064","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04064v2","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04064","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"pith_short_12","alias_value":"F22T6ZWBDQZQ","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"pith_short_16","alias_value":"F22T6ZWBDQZQDEMU","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"pith_short_8","alias_value":"F22T6ZWB","created_at":"2026-07-05T07:23:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:F22T6ZWBDQZQDEMUY33H353BFY","target":"record","payload":{"canonical_record":{"source":{"id":"2306.04064","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-06T23:24:02Z","cross_cats_sorted":[],"title_canon_sha256":"73cf71d8e8a12e298ab3be4d3e00f787d0cf7287dbb675d94156b4c6ad184a40","abstract_canon_sha256":"b17e16c55d386e03e373e466ced773186c67454690c18c036843ef3f68462bff"},"schema_version":"1.0"},"canonical_sha256":"2eb53f66c11c33019194c6f67df7612e1126ea14d72e623d2d728abe4eacae3d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:26.634606Z","signature_b64":"X2v7x1tL+ZFqNtDpaq2JFmYpn9chDL3m8zlgEUO+IJzftSNzgDRoMqbD7NWTbmyKhtkXIFeKXj1BszoGVNnyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2eb53f66c11c33019194c6f67df7612e1126ea14d72e623d2d728abe4eacae3d","last_reissued_at":"2026-07-05T07:23:26.634177Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:26.634177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.04064","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":"2026-07-05T07:23:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+UFg/TMHK73C/uz85qpGIn9qVpOK3xbjqFKFECJoUs92bzSFF8mhiDMQA/S3rikWAs9VNBmjwIXll087o+7EBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:53:24.700691Z"},"content_sha256":"1ef0eabaf8722c74675c0375c70289fd8b901cd5a9ee4f99c9ccb7852b81e97b","schema_version":"1.0","event_id":"sha256:1ef0eabaf8722c74675c0375c70289fd8b901cd5a9ee4f99c9ccb7852b81e97b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:F22T6ZWBDQZQDEMUY33H353BFY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Carmela Troncoso, Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion","submitted_at":"2023-06-06T23:24:02Z","abstract_excerpt":"Research on adversarial robustness is primarily focused on image and text data. Yet, many scenarios in which lack of robustness can result in serious risks, such as fraud detection, medical diagnosis, or recommender systems often do not rely on images or text but instead on tabular data. Adversarial robustness in tabular data poses two serious challenges. First, tabular datasets often contain categorical features, and therefore cannot be tackled directly with existing optimization procedures. Second, in the tabular domain, algorithms that are not based on deep networks are widely used and offe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04064","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.04064/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":"2026-07-05T07:23:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E3XU7I59b/wjpKYNxCfZ20bD4u0xYwh9RoU4Lr1JrAPdxO5YsIZo2CKycKeJIyOgW+I/Rk0LRvmDgymyOHOmBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T08:53:24.701060Z"},"content_sha256":"5c9fb43b041a1516aa369dabe3bea0a9a0865901bd800b78e43e1d4e566eb692","schema_version":"1.0","event_id":"sha256:5c9fb43b041a1516aa369dabe3bea0a9a0865901bd800b78e43e1d4e566eb692"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F22T6ZWBDQZQDEMUY33H353BFY/bundle.json","state_url":"https://pith.science/pith/F22T6ZWBDQZQDEMUY33H353BFY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F22T6ZWBDQZQDEMUY33H353BFY/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":"2026-07-06T08:53:24Z","links":{"resolver":"https://pith.science/pith/F22T6ZWBDQZQDEMUY33H353BFY","bundle":"https://pith.science/pith/F22T6ZWBDQZQDEMUY33H353BFY/bundle.json","state":"https://pith.science/pith/F22T6ZWBDQZQDEMUY33H353BFY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F22T6ZWBDQZQDEMUY33H353BFY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:F22T6ZWBDQZQDEMUY33H353BFY","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":"b17e16c55d386e03e373e466ced773186c67454690c18c036843ef3f68462bff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-06T23:24:02Z","title_canon_sha256":"73cf71d8e8a12e298ab3be4d3e00f787d0cf7287dbb675d94156b4c6ad184a40"},"schema_version":"1.0","source":{"id":"2306.04064","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.04064","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"arxiv_version","alias_value":"2306.04064v2","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.04064","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"pith_short_12","alias_value":"F22T6ZWBDQZQ","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"pith_short_16","alias_value":"F22T6ZWBDQZQDEMU","created_at":"2026-07-05T07:23:26Z"},{"alias_kind":"pith_short_8","alias_value":"F22T6ZWB","created_at":"2026-07-05T07:23:26Z"}],"graph_snapshots":[{"event_id":"sha256:5c9fb43b041a1516aa369dabe3bea0a9a0865901bd800b78e43e1d4e566eb692","target":"graph","created_at":"2026-07-05T07:23:26Z","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/2306.04064/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Research on adversarial robustness is primarily focused on image and text data. Yet, many scenarios in which lack of robustness can result in serious risks, such as fraud detection, medical diagnosis, or recommender systems often do not rely on images or text but instead on tabular data. Adversarial robustness in tabular data poses two serious challenges. First, tabular datasets often contain categorical features, and therefore cannot be tackled directly with existing optimization procedures. Second, in the tabular domain, algorithms that are not based on deep networks are widely used and offe","authors_text":"Carmela Troncoso, Klim Kireev, Maksym Andriushchenko, Nicolas Flammarion","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-06T23:24:02Z","title":"Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.04064","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":"sha256:1ef0eabaf8722c74675c0375c70289fd8b901cd5a9ee4f99c9ccb7852b81e97b","target":"record","created_at":"2026-07-05T07:23:26Z","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":"b17e16c55d386e03e373e466ced773186c67454690c18c036843ef3f68462bff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-06T23:24:02Z","title_canon_sha256":"73cf71d8e8a12e298ab3be4d3e00f787d0cf7287dbb675d94156b4c6ad184a40"},"schema_version":"1.0","source":{"id":"2306.04064","kind":"arxiv","version":2}},"canonical_sha256":"2eb53f66c11c33019194c6f67df7612e1126ea14d72e623d2d728abe4eacae3d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2eb53f66c11c33019194c6f67df7612e1126ea14d72e623d2d728abe4eacae3d","first_computed_at":"2026-07-05T07:23:26.634177Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:23:26.634177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X2v7x1tL+ZFqNtDpaq2JFmYpn9chDL3m8zlgEUO+IJzftSNzgDRoMqbD7NWTbmyKhtkXIFeKXj1BszoGVNnyCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:23:26.634606Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.04064","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1ef0eabaf8722c74675c0375c70289fd8b901cd5a9ee4f99c9ccb7852b81e97b","sha256:5c9fb43b041a1516aa369dabe3bea0a9a0865901bd800b78e43e1d4e566eb692"],"state_sha256":"ba56466a3138ba47431875dbb72b2eb3accf103de09e4eb1949d82c487913367"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XFYVakMYgQTIq5p90HyDfWkmB5AloKoKmex6LkcofAVwrugg9oBeznR9Yx9tdl7W10ICAiOAOn0TOkxOrDLHBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T08:53:24.702986Z","bundle_sha256":"c1b77a2d6f29e7205de7bfb6897e42c749b58e5cf4816c486b883183b02c1803"}}