{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:O77IX5V4OMCPECJ2O3QQNOOURP","short_pith_number":"pith:O77IX5V4","canonical_record":{"source":{"id":"1805.07833","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T22:50:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e3ae59402da247c70efaebd90a63042fcbf902c76db2832ee90883a96acf897a","abstract_canon_sha256":"938958d1e1817c8e9cad6b3123f5cdbcb11dc9634f02d10756663b11cef34f67"},"schema_version":"1.0"},"canonical_sha256":"77fe8bf6bc7304f2093a76e106b9d48bd4bc314dcabea40bb0146004b0f7af38","source":{"kind":"arxiv","id":"1805.07833","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.07833","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"arxiv_version","alias_value":"1805.07833v3","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07833","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"pith_short_12","alias_value":"O77IX5V4OMCP","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"O77IX5V4OMCPECJ2","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"O77IX5V4","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:O77IX5V4OMCPECJ2O3QQNOOURP","target":"record","payload":{"canonical_record":{"source":{"id":"1805.07833","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T22:50:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e3ae59402da247c70efaebd90a63042fcbf902c76db2832ee90883a96acf897a","abstract_canon_sha256":"938958d1e1817c8e9cad6b3123f5cdbcb11dc9634f02d10756663b11cef34f67"},"schema_version":"1.0"},"canonical_sha256":"77fe8bf6bc7304f2093a76e106b9d48bd4bc314dcabea40bb0146004b0f7af38","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:47.935712Z","signature_b64":"Ta6iyx+KnwBi0+w1OZJSGZIaskQryQl6U0qe4pX3DfktE7tUJvQWEbubjdK6OaMoqmkFVYDJ7sRBRSjS6qTJDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77fe8bf6bc7304f2093a76e106b9d48bd4bc314dcabea40bb0146004b0f7af38","last_reissued_at":"2026-05-17T23:56:47.935244Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:47.935244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.07833","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:56:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6Cgs2l3Wb/5DMufj+DhM8Cuu4ueN1j+nCvUyow8igwa0Y6HW6NfLwPziy6ooJcf9eoCsg6qknIm2gaDEVG2KDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:21:53.006748Z"},"content_sha256":"bd2cb2e5e987a484375b11e99ec15895ea5fa79b87c20abd27b7b73831573438","schema_version":"1.0","event_id":"sha256:bd2cb2e5e987a484375b11e99ec15895ea5fa79b87c20abd27b7b73831573438"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:O77IX5V4OMCPECJ2O3QQNOOURP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Wasserstein regularization for sparse multi-task regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alexandre Gramfort, Hicham Janati, Marco Cuturi","submitted_at":"2018-05-20T22:50:18Z","abstract_excerpt":"We focus in this paper on high-dimensional regression problems where each regressor can be associated to a location in a physical space, or more generally a generic geometric space. Such problems often employ sparse priors, which promote models using a small subset of regressors. To increase statistical power, the so-called multi-task techniques were proposed, which consist in the simultaneous estimation of several related models. Combined with sparsity assumptions, it lead to models enforcing the active regressors to be shared across models, thanks to, for instance L1 / Lq norms. We argue in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07833","kind":"arxiv","version":3},"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":"2026-05-17T23:56:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CRtfIPJqy7rRLiGS0vvofO4rM1uhqZw6rs9jLGC/pBYHVC7M5Ra92Ej+U/svstyCwqBf/xOzdGUU9NhDdwP0DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T17:21:53.007434Z"},"content_sha256":"0fa68ec98128539cf57c853b52ee3fe2e0ca62ba0a30e9c48bd452ef64cbf1a7","schema_version":"1.0","event_id":"sha256:0fa68ec98128539cf57c853b52ee3fe2e0ca62ba0a30e9c48bd452ef64cbf1a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O77IX5V4OMCPECJ2O3QQNOOURP/bundle.json","state_url":"https://pith.science/pith/O77IX5V4OMCPECJ2O3QQNOOURP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O77IX5V4OMCPECJ2O3QQNOOURP/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":"2026-05-25T17:21:53Z","links":{"resolver":"https://pith.science/pith/O77IX5V4OMCPECJ2O3QQNOOURP","bundle":"https://pith.science/pith/O77IX5V4OMCPECJ2O3QQNOOURP/bundle.json","state":"https://pith.science/pith/O77IX5V4OMCPECJ2O3QQNOOURP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O77IX5V4OMCPECJ2O3QQNOOURP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:O77IX5V4OMCPECJ2O3QQNOOURP","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":"938958d1e1817c8e9cad6b3123f5cdbcb11dc9634f02d10756663b11cef34f67","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T22:50:18Z","title_canon_sha256":"e3ae59402da247c70efaebd90a63042fcbf902c76db2832ee90883a96acf897a"},"schema_version":"1.0","source":{"id":"1805.07833","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.07833","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"arxiv_version","alias_value":"1805.07833v3","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07833","created_at":"2026-05-17T23:56:47Z"},{"alias_kind":"pith_short_12","alias_value":"O77IX5V4OMCP","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"O77IX5V4OMCPECJ2","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"O77IX5V4","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:0fa68ec98128539cf57c853b52ee3fe2e0ca62ba0a30e9c48bd452ef64cbf1a7","target":"graph","created_at":"2026-05-17T23:56:47Z","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 focus in this paper on high-dimensional regression problems where each regressor can be associated to a location in a physical space, or more generally a generic geometric space. Such problems often employ sparse priors, which promote models using a small subset of regressors. To increase statistical power, the so-called multi-task techniques were proposed, which consist in the simultaneous estimation of several related models. Combined with sparsity assumptions, it lead to models enforcing the active regressors to be shared across models, thanks to, for instance L1 / Lq norms. We argue in ","authors_text":"Alexandre Gramfort, Hicham Janati, Marco Cuturi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T22:50:18Z","title":"Wasserstein regularization for sparse multi-task regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07833","kind":"arxiv","version":3},"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:bd2cb2e5e987a484375b11e99ec15895ea5fa79b87c20abd27b7b73831573438","target":"record","created_at":"2026-05-17T23:56:47Z","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":"938958d1e1817c8e9cad6b3123f5cdbcb11dc9634f02d10756663b11cef34f67","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-20T22:50:18Z","title_canon_sha256":"e3ae59402da247c70efaebd90a63042fcbf902c76db2832ee90883a96acf897a"},"schema_version":"1.0","source":{"id":"1805.07833","kind":"arxiv","version":3}},"canonical_sha256":"77fe8bf6bc7304f2093a76e106b9d48bd4bc314dcabea40bb0146004b0f7af38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77fe8bf6bc7304f2093a76e106b9d48bd4bc314dcabea40bb0146004b0f7af38","first_computed_at":"2026-05-17T23:56:47.935244Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:47.935244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ta6iyx+KnwBi0+w1OZJSGZIaskQryQl6U0qe4pX3DfktE7tUJvQWEbubjdK6OaMoqmkFVYDJ7sRBRSjS6qTJDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:47.935712Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.07833","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd2cb2e5e987a484375b11e99ec15895ea5fa79b87c20abd27b7b73831573438","sha256:0fa68ec98128539cf57c853b52ee3fe2e0ca62ba0a30e9c48bd452ef64cbf1a7"],"state_sha256":"65bf0c794b66336e59a279b3dc757422e4eaac5b52877c8a196a23f0aad59901"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CLWWF4Vp43H3ZXSEjRWhmPv849P0teneZWbw07J/58GM9VOkVOHV8LkUm+VQoGvSHwd6/ul4oxQ+kdD6FI6kBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T17:21:53.011052Z","bundle_sha256":"26a64e12e0da489f3f9d3cfcd21ae3cfd31e9482d6debfdef2f0c77f8e877031"}}