{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QI3GCBMBWRMMWR2G2A4VV7VLIU","short_pith_number":"pith:QI3GCBMB","canonical_record":{"source":{"id":"1709.01215","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T02:18:06Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"270e491eac9a3638e1594008d26b99445d8c81bd8e807fdd9d7311ab38baf82f","abstract_canon_sha256":"4494ca53259d323a740d0962bd6e84b86fb1695d10ec7dfaddf61f5dd4579dea"},"schema_version":"1.0"},"canonical_sha256":"8236610581b458cb4746d0395afeab451a78a54159dc71e41ca8e43106aafcbe","source":{"kind":"arxiv","id":"1709.01215","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.01215","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"arxiv_version","alias_value":"1709.01215v2","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01215","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"pith_short_12","alias_value":"QI3GCBMBWRMM","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QI3GCBMBWRMMWR2G","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QI3GCBMB","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QI3GCBMBWRMMWR2G2A4VV7VLIU","target":"record","payload":{"canonical_record":{"source":{"id":"1709.01215","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T02:18:06Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.NE"],"title_canon_sha256":"270e491eac9a3638e1594008d26b99445d8c81bd8e807fdd9d7311ab38baf82f","abstract_canon_sha256":"4494ca53259d323a740d0962bd6e84b86fb1695d10ec7dfaddf61f5dd4579dea"},"schema_version":"1.0"},"canonical_sha256":"8236610581b458cb4746d0395afeab451a78a54159dc71e41ca8e43106aafcbe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:18.790952Z","signature_b64":"ect9nkI4Suvf+KwNZE1JixYtboEFLJJvxUbJn18IJHTkNfQQLIXZ8hLoGyh4eFsY8fO5fPma8XZ8vs9ZAOIYCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8236610581b458cb4746d0395afeab451a78a54159dc71e41ca8e43106aafcbe","last_reissued_at":"2026-05-18T00:31:18.790198Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:18.790198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1709.01215","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":"2026-05-18T00:31:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AZ4uua9bRuJEpzm1nB7UbJyd0ZMxXEnGSI7F22DdBPuf/nhOyDqmn1BcM74UOXTGT23jS9Ps1+Fq0FDKe2UeAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T21:30:51.402622Z"},"content_sha256":"1155c91fd4becadcd3258b5edb8ec2b08f9617ead5e8659b5820d03bf900d4cf","schema_version":"1.0","event_id":"sha256:1155c91fd4becadcd3258b5edb8ec2b08f9617ead5e8659b5820d03bf900d4cf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QI3GCBMBWRMMWR2G2A4VV7VLIU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Changyou Chen, Chunyuan Li, Hao Liu, Lawrence Carin, Liqun Chen, Ricardo Henao, Yunchen Pu","submitted_at":"2017-09-05T02:18:06Z","abstract_excerpt":"We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01215","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":"2026-05-18T00:31:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RsbPo7ahmnU5/vEyJX0DrLkzSwRacqYrEHxrtJn6qO9WD0C+4uOpa37xZuHFsPC5tNQ+fIw2aSkIbF8aSSW9CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T21:30:51.403011Z"},"content_sha256":"30efca94faf9fec6013808975b14b57bfb2fe0bc4e544dd78ccb4b2ba8839a30","schema_version":"1.0","event_id":"sha256:30efca94faf9fec6013808975b14b57bfb2fe0bc4e544dd78ccb4b2ba8839a30"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QI3GCBMBWRMMWR2G2A4VV7VLIU/bundle.json","state_url":"https://pith.science/pith/QI3GCBMBWRMMWR2G2A4VV7VLIU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QI3GCBMBWRMMWR2G2A4VV7VLIU/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":"2026-06-04T21:30:51Z","links":{"resolver":"https://pith.science/pith/QI3GCBMBWRMMWR2G2A4VV7VLIU","bundle":"https://pith.science/pith/QI3GCBMBWRMMWR2G2A4VV7VLIU/bundle.json","state":"https://pith.science/pith/QI3GCBMBWRMMWR2G2A4VV7VLIU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QI3GCBMBWRMMWR2G2A4VV7VLIU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QI3GCBMBWRMMWR2G2A4VV7VLIU","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":"4494ca53259d323a740d0962bd6e84b86fb1695d10ec7dfaddf61f5dd4579dea","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T02:18:06Z","title_canon_sha256":"270e491eac9a3638e1594008d26b99445d8c81bd8e807fdd9d7311ab38baf82f"},"schema_version":"1.0","source":{"id":"1709.01215","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1709.01215","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"arxiv_version","alias_value":"1709.01215v2","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.01215","created_at":"2026-05-18T00:31:18Z"},{"alias_kind":"pith_short_12","alias_value":"QI3GCBMBWRMM","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QI3GCBMBWRMMWR2G","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QI3GCBMB","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:30efca94faf9fec6013808975b14b57bfb2fe0bc4e544dd78ccb4b2ba8839a30","target":"graph","created_at":"2026-05-18T00:31:18Z","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 investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validat","authors_text":"Changyou Chen, Chunyuan Li, Hao Liu, Lawrence Carin, Liqun Chen, Ricardo Henao, Yunchen Pu","cross_cats":["cs.AI","cs.CV","cs.LG","cs.NE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T02:18:06Z","title":"ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.01215","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":"sha256:1155c91fd4becadcd3258b5edb8ec2b08f9617ead5e8659b5820d03bf900d4cf","target":"record","created_at":"2026-05-18T00:31:18Z","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":"4494ca53259d323a740d0962bd6e84b86fb1695d10ec7dfaddf61f5dd4579dea","cross_cats_sorted":["cs.AI","cs.CV","cs.LG","cs.NE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-09-05T02:18:06Z","title_canon_sha256":"270e491eac9a3638e1594008d26b99445d8c81bd8e807fdd9d7311ab38baf82f"},"schema_version":"1.0","source":{"id":"1709.01215","kind":"arxiv","version":2}},"canonical_sha256":"8236610581b458cb4746d0395afeab451a78a54159dc71e41ca8e43106aafcbe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8236610581b458cb4746d0395afeab451a78a54159dc71e41ca8e43106aafcbe","first_computed_at":"2026-05-18T00:31:18.790198Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:18.790198Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ect9nkI4Suvf+KwNZE1JixYtboEFLJJvxUbJn18IJHTkNfQQLIXZ8hLoGyh4eFsY8fO5fPma8XZ8vs9ZAOIYCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:18.790952Z","signed_message":"canonical_sha256_bytes"},"source_id":"1709.01215","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1155c91fd4becadcd3258b5edb8ec2b08f9617ead5e8659b5820d03bf900d4cf","sha256:30efca94faf9fec6013808975b14b57bfb2fe0bc4e544dd78ccb4b2ba8839a30"],"state_sha256":"2e0b9a8051da51412dd7844da8e90d9f0d4b587106576ab6e572bb072720189d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8tw/wxRSIvpHSkHpNrLthdS7KIE3Y4ppd5KWiDQxxoDR4aufCVixrPUa9BMC9mdxjLZX84YWI6BR6hRWd6VVBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T21:30:51.405048Z","bundle_sha256":"d8402bbba890e15555a2b3b81f410f3b20c7295cef2f6f58d4962a938b859be9"}}