{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CYBYWRPVDYZJEQDUEPNRIR7Z5X","short_pith_number":"pith:CYBYWRPV","canonical_record":{"source":{"id":"2409.02708","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-04T13:44:22Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"95667a44647c17f518221782e84d080046589921fa73bbdbe32e2f6db5d1add4","abstract_canon_sha256":"aff2b6f3f9c67b9578171d2cd782420f82f494ee961f65dc39f4a7bea6869fb4"},"schema_version":"1.0"},"canonical_sha256":"16038b45f51e3292407423db1447f9edcec39a053df8f8df302ac04615e7d59c","source":{"kind":"arxiv","id":"2409.02708","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02708","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02708v2","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02708","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"pith_short_12","alias_value":"CYBYWRPVDYZJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"CYBYWRPVDYZJEQDU","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"CYBYWRPV","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CYBYWRPVDYZJEQDUEPNRIR7Z5X","target":"record","payload":{"canonical_record":{"source":{"id":"2409.02708","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-04T13:44:22Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"95667a44647c17f518221782e84d080046589921fa73bbdbe32e2f6db5d1add4","abstract_canon_sha256":"aff2b6f3f9c67b9578171d2cd782420f82f494ee961f65dc39f4a7bea6869fb4"},"schema_version":"1.0"},"canonical_sha256":"16038b45f51e3292407423db1447f9edcec39a053df8f8df302ac04615e7d59c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:39.550473Z","signature_b64":"xkt/QANPlPbDfN7nNzjKBm1As8hNQsKP32Gi4tfhQj5sURpGeEm8X0FFQBPzHTRwgjczff6jkG58v5sVCtZ6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16038b45f51e3292407423db1447f9edcec39a053df8f8df302ac04615e7d59c","last_reissued_at":"2026-05-18T02:44:39.550014Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:39.550014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.02708","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":"2026-05-18T02:44:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jmlSBrACqfcU5TXVr2mHQIrh6f09+g96fjvNDFYzEcoWnYSg8ksadMyvwCH+z30O+QJFZTCpVrZm7RDpS+gWAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:20:51.936407Z"},"content_sha256":"81c8ef601e3359e362a9b794df1aaf47982bfdf172ed8cd5c08c98cc9a5390be","schema_version":"1.0","event_id":"sha256:81c8ef601e3359e362a9b794df1aaf47982bfdf172ed8cd5c08c98cc9a5390be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CYBYWRPVDYZJEQDUEPNRIR7Z5X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"cs.LG","authors_text":"Chaozhi Zhang, Lin Liu, Xiaoqun Zhang","submitted_at":"2024-09-04T13:44:22Z","abstract_excerpt":"Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the availability of big datasets. One effective strategy to mitigate the issue of insufficient data is to first harness information from other data sources possessing certain similarities in the study design stage, and then employ the multi-task or meta learning framework in the analysis stage. In this paper, we focus on multi-task (or multi-source) linear models whose coefficients across tasks share an invariant low-rank component, a popular structural as"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02708","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":"2026-05-18T02:44:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sSA4ZYcgad94XjHT1zarIsCDuuzTRPkhOvXLXXJmeVXOhcK8s6Oz+urko2EX+xxH4Lt153egl88R6YvXn1PsCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T02:20:51.936994Z"},"content_sha256":"933409efe125593686585f6f397568aa8cca080199a03e66bf36dd21e9adfb4d","schema_version":"1.0","event_id":"sha256:933409efe125593686585f6f397568aa8cca080199a03e66bf36dd21e9adfb4d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CYBYWRPVDYZJEQDUEPNRIR7Z5X/bundle.json","state_url":"https://pith.science/pith/CYBYWRPVDYZJEQDUEPNRIR7Z5X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CYBYWRPVDYZJEQDUEPNRIR7Z5X/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":"2026-05-27T02:20:51Z","links":{"resolver":"https://pith.science/pith/CYBYWRPVDYZJEQDUEPNRIR7Z5X","bundle":"https://pith.science/pith/CYBYWRPVDYZJEQDUEPNRIR7Z5X/bundle.json","state":"https://pith.science/pith/CYBYWRPVDYZJEQDUEPNRIR7Z5X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CYBYWRPVDYZJEQDUEPNRIR7Z5X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CYBYWRPVDYZJEQDUEPNRIR7Z5X","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":"aff2b6f3f9c67b9578171d2cd782420f82f494ee961f65dc39f4a7bea6869fb4","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-04T13:44:22Z","title_canon_sha256":"95667a44647c17f518221782e84d080046589921fa73bbdbe32e2f6db5d1add4"},"schema_version":"1.0","source":{"id":"2409.02708","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.02708","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"arxiv_version","alias_value":"2409.02708v2","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.02708","created_at":"2026-05-18T02:44:39Z"},{"alias_kind":"pith_short_12","alias_value":"CYBYWRPVDYZJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"CYBYWRPVDYZJEQDU","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"CYBYWRPV","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:933409efe125593686585f6f397568aa8cca080199a03e66bf36dd21e9adfb4d","target":"graph","created_at":"2026-05-18T02:44:39Z","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":"Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the availability of big datasets. One effective strategy to mitigate the issue of insufficient data is to first harness information from other data sources possessing certain similarities in the study design stage, and then employ the multi-task or meta learning framework in the analysis stage. In this paper, we focus on multi-task (or multi-source) linear models whose coefficients across tasks share an invariant low-rank component, a popular structural as","authors_text":"Chaozhi Zhang, Lin Liu, Xiaoqun Zhang","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-04T13:44:22Z","title":"Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.02708","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":"sha256:81c8ef601e3359e362a9b794df1aaf47982bfdf172ed8cd5c08c98cc9a5390be","target":"record","created_at":"2026-05-18T02:44:39Z","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":"aff2b6f3f9c67b9578171d2cd782420f82f494ee961f65dc39f4a7bea6869fb4","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-04T13:44:22Z","title_canon_sha256":"95667a44647c17f518221782e84d080046589921fa73bbdbe32e2f6db5d1add4"},"schema_version":"1.0","source":{"id":"2409.02708","kind":"arxiv","version":2}},"canonical_sha256":"16038b45f51e3292407423db1447f9edcec39a053df8f8df302ac04615e7d59c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"16038b45f51e3292407423db1447f9edcec39a053df8f8df302ac04615e7d59c","first_computed_at":"2026-05-18T02:44:39.550014Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:39.550014Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xkt/QANPlPbDfN7nNzjKBm1As8hNQsKP32Gi4tfhQj5sURpGeEm8X0FFQBPzHTRwgjczff6jkG58v5sVCtZ6Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:39.550473Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.02708","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:81c8ef601e3359e362a9b794df1aaf47982bfdf172ed8cd5c08c98cc9a5390be","sha256:933409efe125593686585f6f397568aa8cca080199a03e66bf36dd21e9adfb4d"],"state_sha256":"04425d6ddc66381c59174d7e8e8bfaa5ee551f525e40f287e1060bde5a7334f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hH7NdB65g5KH97q3EY7Cze+mULPT8Po2we96OG1NOIHedjCI/EijHFPqMZNOgR5cABKkE3WJqADcdDJ+bSb8Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T02:20:51.940374Z","bundle_sha256":"1940decea0d213c246f7894e86b5545aab93b03e53b541680879c95ee17b22b5"}}