{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:CWFUYKOVI3OTIMRIGM3RSLRHSK","short_pith_number":"pith:CWFUYKOV","canonical_record":{"source":{"id":"1206.6417","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T19:59:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"42d60b335be6a48ca8dc7a3e0fa68dccc39914c7f67907da727a134b0abdc8e1","abstract_canon_sha256":"930c60bde3d0fb7be009d8e373477d8a3a95f2e8c00cc61096d77aaae66d234c"},"schema_version":"1.0"},"canonical_sha256":"158b4c29d546dd3432283337192e2792aeff85ea4487baa6b6a8f1778283ab30","source":{"kind":"arxiv","id":"1206.6417","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.6417","created_at":"2026-05-18T03:52:14Z"},{"alias_kind":"arxiv_version","alias_value":"1206.6417v1","created_at":"2026-05-18T03:52:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.6417","created_at":"2026-05-18T03:52:14Z"},{"alias_kind":"pith_short_12","alias_value":"CWFUYKOVI3OT","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CWFUYKOVI3OTIMRI","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CWFUYKOV","created_at":"2026-05-18T12:27:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:CWFUYKOVI3OTIMRIGM3RSLRHSK","target":"record","payload":{"canonical_record":{"source":{"id":"1206.6417","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T19:59:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"42d60b335be6a48ca8dc7a3e0fa68dccc39914c7f67907da727a134b0abdc8e1","abstract_canon_sha256":"930c60bde3d0fb7be009d8e373477d8a3a95f2e8c00cc61096d77aaae66d234c"},"schema_version":"1.0"},"canonical_sha256":"158b4c29d546dd3432283337192e2792aeff85ea4487baa6b6a8f1778283ab30","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:52:14.307788Z","signature_b64":"qYRJhRupcNV65+cTp7P7k1YXgIYBhWYrmCxGskFnETYHR5SuWo4xNMjsKoKolSdJiiokCnfqRerReTZs7JEFDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"158b4c29d546dd3432283337192e2792aeff85ea4487baa6b6a8f1778283ab30","last_reissued_at":"2026-05-18T03:52:14.307059Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:52:14.307059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1206.6417","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":"2026-05-18T03:52:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XNrnmqDRAw2RvhCWjkeVGWotcvinmYxta1Lpj9P3B/4Xw59E+i4ORO8potLvHNxsf8Q+YREED+se8Lk8sBNfCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T20:59:12.609876Z"},"content_sha256":"1758cd12c3122550e7b9b23c19ae40e25a284bd197aeb9b497aa746140fb4313","schema_version":"1.0","event_id":"sha256:1758cd12c3122550e7b9b23c19ae40e25a284bd197aeb9b497aa746140fb4313"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:CWFUYKOVI3OTIMRIGM3RSLRHSK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Task Grouping and Overlap in Multi-task Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Abhishek Kumar (University of Maryland), Hal Daume III (University of Maryland)","submitted_at":"2012-06-27T19:59:59Z","abstract_excerpt":"In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nation of a finite number of underlying basis tasks. The coefficients of the linear combina- tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on on the assumption that task pa- rameters wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.6417","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":""},"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":"2026-05-18T03:52:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wXPf1kxHpzyRF6ahA8PIwAlACKleyk8iLfRep4PujKl4OzeYZRlGW+QkWpJyNob0RQUmj9TcsbsmxuuR7/9mDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T20:59:12.610489Z"},"content_sha256":"550cd4416b91aaedd156d1ac3bb77fd7f5ab277dbdec6c9274ace303f2e941d2","schema_version":"1.0","event_id":"sha256:550cd4416b91aaedd156d1ac3bb77fd7f5ab277dbdec6c9274ace303f2e941d2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CWFUYKOVI3OTIMRIGM3RSLRHSK/bundle.json","state_url":"https://pith.science/pith/CWFUYKOVI3OTIMRIGM3RSLRHSK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CWFUYKOVI3OTIMRIGM3RSLRHSK/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":"2026-08-07T20:59:12Z","links":{"resolver":"https://pith.science/pith/CWFUYKOVI3OTIMRIGM3RSLRHSK","bundle":"https://pith.science/pith/CWFUYKOVI3OTIMRIGM3RSLRHSK/bundle.json","state":"https://pith.science/pith/CWFUYKOVI3OTIMRIGM3RSLRHSK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CWFUYKOVI3OTIMRIGM3RSLRHSK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:CWFUYKOVI3OTIMRIGM3RSLRHSK","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":"930c60bde3d0fb7be009d8e373477d8a3a95f2e8c00cc61096d77aaae66d234c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T19:59:59Z","title_canon_sha256":"42d60b335be6a48ca8dc7a3e0fa68dccc39914c7f67907da727a134b0abdc8e1"},"schema_version":"1.0","source":{"id":"1206.6417","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.6417","created_at":"2026-05-18T03:52:14Z"},{"alias_kind":"arxiv_version","alias_value":"1206.6417v1","created_at":"2026-05-18T03:52:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.6417","created_at":"2026-05-18T03:52:14Z"},{"alias_kind":"pith_short_12","alias_value":"CWFUYKOVI3OT","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_16","alias_value":"CWFUYKOVI3OTIMRI","created_at":"2026-05-18T12:27:01Z"},{"alias_kind":"pith_short_8","alias_value":"CWFUYKOV","created_at":"2026-05-18T12:27:01Z"}],"graph_snapshots":[{"event_id":"sha256:550cd4416b91aaedd156d1ac3bb77fd7f5ab277dbdec6c9274ace303f2e941d2","target":"graph","created_at":"2026-05-18T03:52:14Z","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":"In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nation of a finite number of underlying basis tasks. The coefficients of the linear combina- tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on on the assumption that task pa- rameters wi","authors_text":"Abhishek Kumar (University of Maryland), Hal Daume III (University of Maryland)","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T19:59:59Z","title":"Learning Task Grouping and Overlap in Multi-task Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.6417","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":"sha256:1758cd12c3122550e7b9b23c19ae40e25a284bd197aeb9b497aa746140fb4313","target":"record","created_at":"2026-05-18T03:52:14Z","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":"930c60bde3d0fb7be009d8e373477d8a3a95f2e8c00cc61096d77aaae66d234c","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-06-27T19:59:59Z","title_canon_sha256":"42d60b335be6a48ca8dc7a3e0fa68dccc39914c7f67907da727a134b0abdc8e1"},"schema_version":"1.0","source":{"id":"1206.6417","kind":"arxiv","version":1}},"canonical_sha256":"158b4c29d546dd3432283337192e2792aeff85ea4487baa6b6a8f1778283ab30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"158b4c29d546dd3432283337192e2792aeff85ea4487baa6b6a8f1778283ab30","first_computed_at":"2026-05-18T03:52:14.307059Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:52:14.307059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qYRJhRupcNV65+cTp7P7k1YXgIYBhWYrmCxGskFnETYHR5SuWo4xNMjsKoKolSdJiiokCnfqRerReTZs7JEFDg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:52:14.307788Z","signed_message":"canonical_sha256_bytes"},"source_id":"1206.6417","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1758cd12c3122550e7b9b23c19ae40e25a284bd197aeb9b497aa746140fb4313","sha256:550cd4416b91aaedd156d1ac3bb77fd7f5ab277dbdec6c9274ace303f2e941d2"],"state_sha256":"6941b527fcb1d3484ff345fd4fae8611d1509c6a0ef7c9b627d3d89833faadff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iluG4sSeDkoFNWLqrDJrjs4BwJuLULFu2s2QujaQx8ILPTaH09F0N63ECyHjyOcrXKlsBfc6ejkUMVC9C2rPDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T20:59:12.615809Z","bundle_sha256":"e29cb6de763632a6655cfd068ea14d3f25c97e22445569e458a51bbd019e7935"}}