{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:NSDZQUP6657IHXSP2ACIUIHOJM","short_pith_number":"pith:NSDZQUP6","canonical_record":{"source":{"id":"1802.02219","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-06T21:02:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab1e89f24c220eadb8fdef48adddaea192287d65e2aeda55d309ff2838b3add9","abstract_canon_sha256":"ff272ed62ceeed526a7360ab7c73f748b800202ae2b0244dacfe480fd0bdd1e4"},"schema_version":"1.0"},"canonical_sha256":"6c879851fef77e83de4fd0048a20ee4b3da49986c97b115991effd95d0ca80c2","source":{"kind":"arxiv","id":"1802.02219","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02219","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02219v4","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02219","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"pith_short_12","alias_value":"NSDZQUP6657I","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"pith_short_16","alias_value":"NSDZQUP6657IHXSP","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"pith_short_8","alias_value":"NSDZQUP6","created_at":"2026-07-05T05:09:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:NSDZQUP6657IHXSP2ACIUIHOJM","target":"record","payload":{"canonical_record":{"source":{"id":"1802.02219","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-06T21:02:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ab1e89f24c220eadb8fdef48adddaea192287d65e2aeda55d309ff2838b3add9","abstract_canon_sha256":"ff272ed62ceeed526a7360ab7c73f748b800202ae2b0244dacfe480fd0bdd1e4"},"schema_version":"1.0"},"canonical_sha256":"6c879851fef77e83de4fd0048a20ee4b3da49986c97b115991effd95d0ca80c2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:10.167139Z","signature_b64":"T6zz4ArkrF5EdQTAkhetJK7UE0Tl0zvzQ+qnSTv+ZazJLbM83wJGgkhLktwQR1HTPEbh8hJiPiqIKUc8Oy3CBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c879851fef77e83de4fd0048a20ee4b3da49986c97b115991effd95d0ca80c2","last_reissued_at":"2026-07-05T05:09:10.166722Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:10.166722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.02219","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:09:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d6y7QM3eO7sE88loXFZ43nD9ztPWDwv0fA9AM/LuXMQTOSH0gPFnnRpCr1KNN6z1oUqiCFWEmClgjOHAAyg/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:26:10.592985Z"},"content_sha256":"f3f442879cd5d2faa7fc97535b0a698d680936eb166bb8e71b0b6d67b979c08e","schema_version":"1.0","event_id":"sha256:f3f442879cd5d2faa7fc97535b0a698d680936eb166bb8e71b0b6d67b979c08e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:NSDZQUP6657IHXSP2ACIUIHOJM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Practical Transfer Learning for Bayesian Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"stat.ML","authors_text":"Benjamin Letham, Eytan Bakshy, Frank Hutter, Matthias Feurer","submitted_at":"2018-02-06T21:02:59Z","abstract_excerpt":"When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extensions to Bayesian optimization and establish a worst-case bound compared to vanilla Bayesian optimization. Using a large collection of hyperparameter optimization benchmark problems, we demonstrate that our contributions substantially reduce optimization time compared to standard "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02219","kind":"arxiv","version":4},"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/1802.02219/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":"2026-07-05T05:09:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LvBXXL+Z0xveYHeekujgywWPKGti4lvm4jP4lcYFpcf95w4TSjFo1od3n+gb+RZILMWc3pGIW9DIZ/igajd1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:26:10.593362Z"},"content_sha256":"6e0549ab240f170968222094e2ec8022fcf88dbe07fbab9506e3dfd1ab40ebe8","schema_version":"1.0","event_id":"sha256:6e0549ab240f170968222094e2ec8022fcf88dbe07fbab9506e3dfd1ab40ebe8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NSDZQUP6657IHXSP2ACIUIHOJM/bundle.json","state_url":"https://pith.science/pith/NSDZQUP6657IHXSP2ACIUIHOJM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NSDZQUP6657IHXSP2ACIUIHOJM/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":"2026-08-07T23:26:10Z","links":{"resolver":"https://pith.science/pith/NSDZQUP6657IHXSP2ACIUIHOJM","bundle":"https://pith.science/pith/NSDZQUP6657IHXSP2ACIUIHOJM/bundle.json","state":"https://pith.science/pith/NSDZQUP6657IHXSP2ACIUIHOJM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NSDZQUP6657IHXSP2ACIUIHOJM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:NSDZQUP6657IHXSP2ACIUIHOJM","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":"ff272ed62ceeed526a7360ab7c73f748b800202ae2b0244dacfe480fd0bdd1e4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-06T21:02:59Z","title_canon_sha256":"ab1e89f24c220eadb8fdef48adddaea192287d65e2aeda55d309ff2838b3add9"},"schema_version":"1.0","source":{"id":"1802.02219","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02219","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02219v4","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02219","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"pith_short_12","alias_value":"NSDZQUP6657I","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"pith_short_16","alias_value":"NSDZQUP6657IHXSP","created_at":"2026-07-05T05:09:10Z"},{"alias_kind":"pith_short_8","alias_value":"NSDZQUP6","created_at":"2026-07-05T05:09:10Z"}],"graph_snapshots":[{"event_id":"sha256:6e0549ab240f170968222094e2ec8022fcf88dbe07fbab9506e3dfd1ab40ebe8","target":"graph","created_at":"2026-07-05T05:09:10Z","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/1802.02219/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"When hyperparameter optimization of a machine learning algorithm is repeated for multiple datasets it is possible to transfer knowledge to an optimization run on a new dataset. We develop a new hyperparameter-free ensemble model for Bayesian optimization that is a generalization of two existing transfer learning extensions to Bayesian optimization and establish a worst-case bound compared to vanilla Bayesian optimization. Using a large collection of hyperparameter optimization benchmark problems, we demonstrate that our contributions substantially reduce optimization time compared to standard ","authors_text":"Benjamin Letham, Eytan Bakshy, Frank Hutter, Matthias Feurer","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-06T21:02:59Z","title":"Practical Transfer Learning for Bayesian Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02219","kind":"arxiv","version":4},"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:f3f442879cd5d2faa7fc97535b0a698d680936eb166bb8e71b0b6d67b979c08e","target":"record","created_at":"2026-07-05T05:09:10Z","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":"ff272ed62ceeed526a7360ab7c73f748b800202ae2b0244dacfe480fd0bdd1e4","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-06T21:02:59Z","title_canon_sha256":"ab1e89f24c220eadb8fdef48adddaea192287d65e2aeda55d309ff2838b3add9"},"schema_version":"1.0","source":{"id":"1802.02219","kind":"arxiv","version":4}},"canonical_sha256":"6c879851fef77e83de4fd0048a20ee4b3da49986c97b115991effd95d0ca80c2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c879851fef77e83de4fd0048a20ee4b3da49986c97b115991effd95d0ca80c2","first_computed_at":"2026-07-05T05:09:10.166722Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:09:10.166722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"T6zz4ArkrF5EdQTAkhetJK7UE0Tl0zvzQ+qnSTv+ZazJLbM83wJGgkhLktwQR1HTPEbh8hJiPiqIKUc8Oy3CBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:09:10.167139Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.02219","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3f442879cd5d2faa7fc97535b0a698d680936eb166bb8e71b0b6d67b979c08e","sha256:6e0549ab240f170968222094e2ec8022fcf88dbe07fbab9506e3dfd1ab40ebe8"],"state_sha256":"9c0f955b57a76134cc710495b62007cb819f6c7279e472d01f26762da1c69fda"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mWoRQUrshxGF8oQ3W8IEFQuApZ+BVqv+TKhCT0mUQjkrJEaKc+bwK9UAYjPUBPzht1nCeIRnL7YWVyZWLej2Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:26:10.596113Z","bundle_sha256":"04a77436a7fdc9c60c0b2399cc6dd47727e121c3f4e4617a6d4adf4cb856b063"}}