{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:QBCS4UMNYNYB53V5TEYEAU75DR","short_pith_number":"pith:QBCS4UMN","canonical_record":{"source":{"id":"2304.02064","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-04T18:32:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ed44937da40d08957169b82c01f3f890bbd6be143df202c32cea57bf6ee81dbb","abstract_canon_sha256":"c99c1ddf1c5693acf9565d5edd8b76deb605a6c940583148c6932fc354a6c606"},"schema_version":"1.0"},"canonical_sha256":"80452e518dc3701eeebd99304053fd1c558da58137cb2d5c8f844dfe26e44f8c","source":{"kind":"arxiv","id":"2304.02064","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.02064","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"arxiv_version","alias_value":"2304.02064v1","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.02064","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"pith_short_12","alias_value":"QBCS4UMNYNYB","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"pith_short_16","alias_value":"QBCS4UMNYNYB53V5","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"pith_short_8","alias_value":"QBCS4UMN","created_at":"2026-07-05T05:58:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:QBCS4UMNYNYB53V5TEYEAU75DR","target":"record","payload":{"canonical_record":{"source":{"id":"2304.02064","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-04T18:32:20Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ed44937da40d08957169b82c01f3f890bbd6be143df202c32cea57bf6ee81dbb","abstract_canon_sha256":"c99c1ddf1c5693acf9565d5edd8b76deb605a6c940583148c6932fc354a6c606"},"schema_version":"1.0"},"canonical_sha256":"80452e518dc3701eeebd99304053fd1c558da58137cb2d5c8f844dfe26e44f8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:58:21.520813Z","signature_b64":"pNmqgx/b8P2ccxXF5q6vRpdtt4P0+xzPck8aCafaxiMi2hV3tX+0mTm0hdedpnRbNrU07ixwwzgjgErzag2KDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80452e518dc3701eeebd99304053fd1c558da58137cb2d5c8f844dfe26e44f8c","last_reissued_at":"2026-07-05T05:58:21.520312Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:58:21.520312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.02064","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":"2026-07-05T05:58:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lb6ibJhuzkrjuxxauYS9F0TBRS/iE51Z0DVzzt/X61pJp1SWxlqXTe6L8O6Lt03pjrZuqYMlCm5yDSzTLKQ+Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:37:18.962433Z"},"content_sha256":"3d253b706edeca991287c9218e4cceada849db5db64f058244e6510dceee0cbd","schema_version":"1.0","event_id":"sha256:3d253b706edeca991287c9218e4cceada849db5db64f058244e6510dceee0cbd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:QBCS4UMNYNYB53V5TEYEAU75DR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Mario Marchand, Qi Chen","submitted_at":"2023-04-04T18:32:20Z","abstract_excerpt":"We use information-theoretic tools to derive a novel analysis of Multi-source Domain Adaptation (MDA) from the representation learning perspective. Concretely, we study joint distribution alignment for supervised MDA with few target labels and unsupervised MDA with pseudo labels, where the latter is relatively hard and less commonly studied. We further provide algorithm-dependent generalization bounds for these two settings, where the generalization is characterized by the mutual information between the parameters and the data. Then we propose a novel deep MDA algorithm, implicitly addressing "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.02064","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.02064/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":"2026-07-05T05:58:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SnAvc1ZvXqQqj7IVvm35P+I8o9bCkEgjseon+0i/O/OqUKnCJGQrxJn6PInXna1fS1z697hNBjH4/Iqa1VKmBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T01:37:18.962953Z"},"content_sha256":"f725b51833be967e232f349c83ae1189a2d1dd65911409fdd989b174caf857ac","schema_version":"1.0","event_id":"sha256:f725b51833be967e232f349c83ae1189a2d1dd65911409fdd989b174caf857ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QBCS4UMNYNYB53V5TEYEAU75DR/bundle.json","state_url":"https://pith.science/pith/QBCS4UMNYNYB53V5TEYEAU75DR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QBCS4UMNYNYB53V5TEYEAU75DR/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":"2026-08-23T01:37:18Z","links":{"resolver":"https://pith.science/pith/QBCS4UMNYNYB53V5TEYEAU75DR","bundle":"https://pith.science/pith/QBCS4UMNYNYB53V5TEYEAU75DR/bundle.json","state":"https://pith.science/pith/QBCS4UMNYNYB53V5TEYEAU75DR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QBCS4UMNYNYB53V5TEYEAU75DR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:QBCS4UMNYNYB53V5TEYEAU75DR","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":"c99c1ddf1c5693acf9565d5edd8b76deb605a6c940583148c6932fc354a6c606","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-04T18:32:20Z","title_canon_sha256":"ed44937da40d08957169b82c01f3f890bbd6be143df202c32cea57bf6ee81dbb"},"schema_version":"1.0","source":{"id":"2304.02064","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.02064","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"arxiv_version","alias_value":"2304.02064v1","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.02064","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"pith_short_12","alias_value":"QBCS4UMNYNYB","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"pith_short_16","alias_value":"QBCS4UMNYNYB53V5","created_at":"2026-07-05T05:58:21Z"},{"alias_kind":"pith_short_8","alias_value":"QBCS4UMN","created_at":"2026-07-05T05:58:21Z"}],"graph_snapshots":[{"event_id":"sha256:f725b51833be967e232f349c83ae1189a2d1dd65911409fdd989b174caf857ac","target":"graph","created_at":"2026-07-05T05:58:21Z","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/2304.02064/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We use information-theoretic tools to derive a novel analysis of Multi-source Domain Adaptation (MDA) from the representation learning perspective. Concretely, we study joint distribution alignment for supervised MDA with few target labels and unsupervised MDA with pseudo labels, where the latter is relatively hard and less commonly studied. We further provide algorithm-dependent generalization bounds for these two settings, where the generalization is characterized by the mutual information between the parameters and the data. Then we propose a novel deep MDA algorithm, implicitly addressing ","authors_text":"Mario Marchand, Qi Chen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-04T18:32:20Z","title":"Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.02064","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":"sha256:3d253b706edeca991287c9218e4cceada849db5db64f058244e6510dceee0cbd","target":"record","created_at":"2026-07-05T05:58:21Z","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":"c99c1ddf1c5693acf9565d5edd8b76deb605a6c940583148c6932fc354a6c606","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-04-04T18:32:20Z","title_canon_sha256":"ed44937da40d08957169b82c01f3f890bbd6be143df202c32cea57bf6ee81dbb"},"schema_version":"1.0","source":{"id":"2304.02064","kind":"arxiv","version":1}},"canonical_sha256":"80452e518dc3701eeebd99304053fd1c558da58137cb2d5c8f844dfe26e44f8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"80452e518dc3701eeebd99304053fd1c558da58137cb2d5c8f844dfe26e44f8c","first_computed_at":"2026-07-05T05:58:21.520312Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:58:21.520312Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pNmqgx/b8P2ccxXF5q6vRpdtt4P0+xzPck8aCafaxiMi2hV3tX+0mTm0hdedpnRbNrU07ixwwzgjgErzag2KDA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:58:21.520813Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.02064","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d253b706edeca991287c9218e4cceada849db5db64f058244e6510dceee0cbd","sha256:f725b51833be967e232f349c83ae1189a2d1dd65911409fdd989b174caf857ac"],"state_sha256":"7fb461db16d4d23abb43ef17370a1445b6f6e8a2d15fe2ba0a9733ab8db3f01b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pD3HMUnacnQQPnlDFwBRtslQvo/MwiMV5vxXHbLGHk/7GgRMDzho5VkPxa7XnIzy5pQVGcx7Vu87nCUqYmyaAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T01:37:18.967337Z","bundle_sha256":"a9bc68989118fe8197018a5a4ba9f306abc26ceb4e46476288126e8beb3b6074"}}