{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:4KHGHAWOZ57WASCH2NM75UV6UU","short_pith_number":"pith:4KHGHAWO","canonical_record":{"source":{"id":"1110.5238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-10-24T14:01:26Z","cross_cats_sorted":[],"title_canon_sha256":"0e56ecf8a152cc464ec04fb650a133940e47379bac5f4a5b75a54e0b1be84375","abstract_canon_sha256":"5854762b0e420e86be73e83d3c2feaea4fbb73c2de8b23be5d3f9053e2b41ed8"},"schema_version":"1.0"},"canonical_sha256":"e28e6382cecf7f604847d359fed2bea52d36adf96dde2f7687ff06d7dccd2726","source":{"kind":"arxiv","id":"1110.5238","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1110.5238","created_at":"2026-05-18T04:10:24Z"},{"alias_kind":"arxiv_version","alias_value":"1110.5238v1","created_at":"2026-05-18T04:10:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1110.5238","created_at":"2026-05-18T04:10:24Z"},{"alias_kind":"pith_short_12","alias_value":"4KHGHAWOZ57W","created_at":"2026-05-18T12:26:20Z"},{"alias_kind":"pith_short_16","alias_value":"4KHGHAWOZ57WASCH","created_at":"2026-05-18T12:26:20Z"},{"alias_kind":"pith_short_8","alias_value":"4KHGHAWO","created_at":"2026-05-18T12:26:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:4KHGHAWOZ57WASCH2NM75UV6UU","target":"record","payload":{"canonical_record":{"source":{"id":"1110.5238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-10-24T14:01:26Z","cross_cats_sorted":[],"title_canon_sha256":"0e56ecf8a152cc464ec04fb650a133940e47379bac5f4a5b75a54e0b1be84375","abstract_canon_sha256":"5854762b0e420e86be73e83d3c2feaea4fbb73c2de8b23be5d3f9053e2b41ed8"},"schema_version":"1.0"},"canonical_sha256":"e28e6382cecf7f604847d359fed2bea52d36adf96dde2f7687ff06d7dccd2726","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:10:24.366669Z","signature_b64":"VFSorlL2rYyws3DwDoJWALHMXjafeHE8PhU1NPfgBKDV1nA3YiskISIGuaPc4RRTZsr7RIYGFAiFIx4SQzIfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e28e6382cecf7f604847d359fed2bea52d36adf96dde2f7687ff06d7dccd2726","last_reissued_at":"2026-05-18T04:10:24.366111Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:10:24.366111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1110.5238","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":"2026-05-18T04:10:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f/+PrCNFaGstqGNYkyiAKpPuZ78lmPyR/l3kN53hrzWsffbe1xEU7Ep1pBPhoned5nPSgcBJTMKy56ElkqOJBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T01:44:38.642131Z"},"content_sha256":"1c8d4795bd8f1beb69fb8eee397c39f03152356c1fa317269c39cce4ff69b7b2","schema_version":"1.0","event_id":"sha256:1c8d4795bd8f1beb69fb8eee397c39f03152356c1fa317269c39cce4ff69b7b2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:4KHGHAWOZ57WASCH2NM75UV6UU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multiple Gaussian Process Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Cedric Archambeau, Francis Bach","submitted_at":"2011-10-24T14:01:26Z","abstract_excerpt":"We consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices that best explains the data and by doing so improve the generalisation on unseen data. Sparsity in the kernel weights is obtained by adopting a hierarchical Bayesian approach: Gaussian process priors are imposed over the latent functions and generalised inverse Gaussians on their associated weights. This construction is equivalent to imposing a product of heavy-tailed process priors over function space. A variational inference algorithm is derived "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1110.5238","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":"2026-05-18T04:10:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YBeMjkwQv1X7Vd2aCS/LORwh5A7wyjKnN937MDagi0NV4gO31AcePongEEqV9FKwzPxHHLGJ8nQJ2E1mcv0EDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T01:44:38.642509Z"},"content_sha256":"2b753b931c04451512df88a59d46ef0a7683e8eee7504676687da17a99d491ba","schema_version":"1.0","event_id":"sha256:2b753b931c04451512df88a59d46ef0a7683e8eee7504676687da17a99d491ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4KHGHAWOZ57WASCH2NM75UV6UU/bundle.json","state_url":"https://pith.science/pith/4KHGHAWOZ57WASCH2NM75UV6UU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4KHGHAWOZ57WASCH2NM75UV6UU/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":"2026-05-28T01:44:38Z","links":{"resolver":"https://pith.science/pith/4KHGHAWOZ57WASCH2NM75UV6UU","bundle":"https://pith.science/pith/4KHGHAWOZ57WASCH2NM75UV6UU/bundle.json","state":"https://pith.science/pith/4KHGHAWOZ57WASCH2NM75UV6UU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4KHGHAWOZ57WASCH2NM75UV6UU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:4KHGHAWOZ57WASCH2NM75UV6UU","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":"5854762b0e420e86be73e83d3c2feaea4fbb73c2de8b23be5d3f9053e2b41ed8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-10-24T14:01:26Z","title_canon_sha256":"0e56ecf8a152cc464ec04fb650a133940e47379bac5f4a5b75a54e0b1be84375"},"schema_version":"1.0","source":{"id":"1110.5238","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1110.5238","created_at":"2026-05-18T04:10:24Z"},{"alias_kind":"arxiv_version","alias_value":"1110.5238v1","created_at":"2026-05-18T04:10:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1110.5238","created_at":"2026-05-18T04:10:24Z"},{"alias_kind":"pith_short_12","alias_value":"4KHGHAWOZ57W","created_at":"2026-05-18T12:26:20Z"},{"alias_kind":"pith_short_16","alias_value":"4KHGHAWOZ57WASCH","created_at":"2026-05-18T12:26:20Z"},{"alias_kind":"pith_short_8","alias_value":"4KHGHAWO","created_at":"2026-05-18T12:26:20Z"}],"graph_snapshots":[{"event_id":"sha256:2b753b931c04451512df88a59d46ef0a7683e8eee7504676687da17a99d491ba","target":"graph","created_at":"2026-05-18T04:10:24Z","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 consider a Gaussian process formulation of the multiple kernel learning problem. The goal is to select the convex combination of kernel matrices that best explains the data and by doing so improve the generalisation on unseen data. Sparsity in the kernel weights is obtained by adopting a hierarchical Bayesian approach: Gaussian process priors are imposed over the latent functions and generalised inverse Gaussians on their associated weights. This construction is equivalent to imposing a product of heavy-tailed process priors over function space. A variational inference algorithm is derived ","authors_text":"Cedric Archambeau, Francis Bach","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-10-24T14:01:26Z","title":"Multiple Gaussian Process Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1110.5238","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":"sha256:1c8d4795bd8f1beb69fb8eee397c39f03152356c1fa317269c39cce4ff69b7b2","target":"record","created_at":"2026-05-18T04:10:24Z","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":"5854762b0e420e86be73e83d3c2feaea4fbb73c2de8b23be5d3f9053e2b41ed8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-10-24T14:01:26Z","title_canon_sha256":"0e56ecf8a152cc464ec04fb650a133940e47379bac5f4a5b75a54e0b1be84375"},"schema_version":"1.0","source":{"id":"1110.5238","kind":"arxiv","version":1}},"canonical_sha256":"e28e6382cecf7f604847d359fed2bea52d36adf96dde2f7687ff06d7dccd2726","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e28e6382cecf7f604847d359fed2bea52d36adf96dde2f7687ff06d7dccd2726","first_computed_at":"2026-05-18T04:10:24.366111Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:10:24.366111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VFSorlL2rYyws3DwDoJWALHMXjafeHE8PhU1NPfgBKDV1nA3YiskISIGuaPc4RRTZsr7RIYGFAiFIx4SQzIfBA==","signature_status":"signed_v1","signed_at":"2026-05-18T04:10:24.366669Z","signed_message":"canonical_sha256_bytes"},"source_id":"1110.5238","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c8d4795bd8f1beb69fb8eee397c39f03152356c1fa317269c39cce4ff69b7b2","sha256:2b753b931c04451512df88a59d46ef0a7683e8eee7504676687da17a99d491ba"],"state_sha256":"e33c432c27c9a71380e441b9ba4396a340e511b14835500bba6442aa2ff711b5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FQmypva0zDvJGDdyG4CWjlfwIepvQLLxAUr4VTNw+l5qNkqKVuFUKdk7QF+uun/liAUI4zaF1QLYzMwj6hyFDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T01:44:38.644519Z","bundle_sha256":"1884e5eb0bf9272dd71f8654634b67d463bb6fe3c12671d358f1bdfc79bee748"}}