{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:2YSYRW2X53IC4V7WPDUUEYM7Z3","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":"6ea145908ad4f8c554b05e5f63f4133f95ab9fd2b3f9397bf824c447299902c4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T16:05:43Z","title_canon_sha256":"32d256b4706016655454543a066f13c897b6c8e09f423491ae2832d3759898cf"},"schema_version":"1.0","source":{"id":"2205.07774","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.07774","created_at":"2026-07-05T04:23:32Z"},{"alias_kind":"arxiv_version","alias_value":"2205.07774v1","created_at":"2026-07-05T04:23:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.07774","created_at":"2026-07-05T04:23:32Z"},{"alias_kind":"pith_short_12","alias_value":"2YSYRW2X53IC","created_at":"2026-07-05T04:23:32Z"},{"alias_kind":"pith_short_16","alias_value":"2YSYRW2X53IC4V7W","created_at":"2026-07-05T04:23:32Z"},{"alias_kind":"pith_short_8","alias_value":"2YSYRW2X","created_at":"2026-07-05T04:23:32Z"}],"graph_snapshots":[{"event_id":"sha256:bde52393d9899d77b5a3bafa8da121277e09e1702c94cae26d9f87b8c38c259f","target":"graph","created_at":"2026-07-05T04:23:32Z","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/2205.07774/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Counterfactual examples are an appealing class of post-hoc explanations for machine learning models. Given input $x$ of class $y_1$, its counterfactual is a contrastive example $x^\\prime$ of another class $y_0$. Current approaches primarily solve this task by a complex optimization: define an objective function based on the loss of the counterfactual outcome $y_0$ with hard or soft constraints, then optimize this function as a black-box. This \"deep learning\" approach, however, is rather slow, sometimes tricky, and may result in unrealistic counterfactual examples. In this work, we propose a no","authors_text":"Kristian Kersting, Xiaoting Shao","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T16:05:43Z","title":"Gradient-based Counterfactual Explanations using Tractable Probabilistic Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.07774","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:d32f781e5f6d106380e17f39664b65b86b27b249273802e0fe752d6b3a77b49a","target":"record","created_at":"2026-07-05T04:23:32Z","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":"6ea145908ad4f8c554b05e5f63f4133f95ab9fd2b3f9397bf824c447299902c4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-05-16T16:05:43Z","title_canon_sha256":"32d256b4706016655454543a066f13c897b6c8e09f423491ae2832d3759898cf"},"schema_version":"1.0","source":{"id":"2205.07774","kind":"arxiv","version":1}},"canonical_sha256":"d62588db57eed02e57f678e942619fcee3b7825ba4ef3b896445d062f6a130f7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d62588db57eed02e57f678e942619fcee3b7825ba4ef3b896445d062f6a130f7","first_computed_at":"2026-07-05T04:23:32.084580Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:23:32.084580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PyrP0lhZnHQ2+TaO9ofyw4A877azY6NmU9wXqizI2/en7i0fsuDbIPYoMRGabIKKbjud8f0+2WcwF5VxYgyKBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:23:32.084982Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.07774","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d32f781e5f6d106380e17f39664b65b86b27b249273802e0fe752d6b3a77b49a","sha256:bde52393d9899d77b5a3bafa8da121277e09e1702c94cae26d9f87b8c38c259f"],"state_sha256":"4d5d447ec9f186f07558b55af2a381cd85e2403b828ed794d665244ca815a477"}