{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:H6VGKLY5T6PPXX7VLRVJD7HYAT","short_pith_number":"pith:H6VGKLY5","canonical_record":{"source":{"id":"1407.7969","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-07-30T08:29:38Z","cross_cats_sorted":[],"title_canon_sha256":"a6ad30491bc4cecc4e011640512a8552ab5ca511eb92ff170a09e1905fda6b31","abstract_canon_sha256":"c4fe4e48900bf3d70a41739ec76ed215a8e0e5f6ba481bae18b83ba604e2a13b"},"schema_version":"1.0"},"canonical_sha256":"3faa652f1d9f9efbdff55c6a91fcf804c16c52681c20817738746e9e29bf7f4a","source":{"kind":"arxiv","id":"1407.7969","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.7969","created_at":"2026-05-18T02:46:13Z"},{"alias_kind":"arxiv_version","alias_value":"1407.7969v1","created_at":"2026-05-18T02:46:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.7969","created_at":"2026-05-18T02:46:13Z"},{"alias_kind":"pith_short_12","alias_value":"H6VGKLY5T6PP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"H6VGKLY5T6PPXX7V","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"H6VGKLY5","created_at":"2026-05-18T12:28:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:H6VGKLY5T6PPXX7VLRVJD7HYAT","target":"record","payload":{"canonical_record":{"source":{"id":"1407.7969","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-07-30T08:29:38Z","cross_cats_sorted":[],"title_canon_sha256":"a6ad30491bc4cecc4e011640512a8552ab5ca511eb92ff170a09e1905fda6b31","abstract_canon_sha256":"c4fe4e48900bf3d70a41739ec76ed215a8e0e5f6ba481bae18b83ba604e2a13b"},"schema_version":"1.0"},"canonical_sha256":"3faa652f1d9f9efbdff55c6a91fcf804c16c52681c20817738746e9e29bf7f4a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:46:13.928052Z","signature_b64":"0jJxOr92hoXFuU+idGOLjfcrTZBq/dTDYI1oIdQOW859KVYG/odprYaL/m+n8xNluvmAQRqN5kR9jFA8AdXgCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3faa652f1d9f9efbdff55c6a91fcf804c16c52681c20817738746e9e29bf7f4a","last_reissued_at":"2026-05-18T02:46:13.927311Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:46:13.927311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1407.7969","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":"2026-05-18T02:46:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"crqzyBIGhZAOXHXhOLA6yj91g5rZL2aftXciuQtAfWIRQajxxSkTDa8w/KaC88ws1Ldt8DtWslrFYyL3PMZCCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:07:26.584120Z"},"content_sha256":"0151b1b0c0a486465d6cfb58834641d3d32b8cb18cc34f379a6ed8d1628a8ef8","schema_version":"1.0","event_id":"sha256:0151b1b0c0a486465d6cfb58834641d3d32b8cb18cc34f379a6ed8d1628a8ef8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:H6VGKLY5T6PPXX7VLRVJD7HYAT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automated Machine Learning on Big Data using Stochastic Algorithm Tuning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Michael A Osborne, Stephen J Roberts, Steven Reece, Thomas Nickson","submitted_at":"2014-07-30T08:29:38Z","abstract_excerpt":"We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious hand-tuning, brute search or lengthy sampling runs. Against this background, Bayesian optimisation is finding increasing use in automating parameter tuning, making ML algorithms accessible even to non-experts. However, the state of the art in Bayesian optimisation is incapable of sca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.7969","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":"2026-05-18T02:46:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mbolhZgFWwTbyXTyQj5GHZ+ePuBQ74EO734GrEEwvicSj/6cXdfPsMeBHoBRlFmdun+TQZAg7UuoULBpTjZ5Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:07:26.584831Z"},"content_sha256":"544fc10444577e2726b678822b33e27e6d364dfcd4f460521d35aeec46fc50e1","schema_version":"1.0","event_id":"sha256:544fc10444577e2726b678822b33e27e6d364dfcd4f460521d35aeec46fc50e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H6VGKLY5T6PPXX7VLRVJD7HYAT/bundle.json","state_url":"https://pith.science/pith/H6VGKLY5T6PPXX7VLRVJD7HYAT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H6VGKLY5T6PPXX7VLRVJD7HYAT/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":"2026-05-26T07:07:26Z","links":{"resolver":"https://pith.science/pith/H6VGKLY5T6PPXX7VLRVJD7HYAT","bundle":"https://pith.science/pith/H6VGKLY5T6PPXX7VLRVJD7HYAT/bundle.json","state":"https://pith.science/pith/H6VGKLY5T6PPXX7VLRVJD7HYAT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H6VGKLY5T6PPXX7VLRVJD7HYAT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:H6VGKLY5T6PPXX7VLRVJD7HYAT","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":"c4fe4e48900bf3d70a41739ec76ed215a8e0e5f6ba481bae18b83ba604e2a13b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-07-30T08:29:38Z","title_canon_sha256":"a6ad30491bc4cecc4e011640512a8552ab5ca511eb92ff170a09e1905fda6b31"},"schema_version":"1.0","source":{"id":"1407.7969","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1407.7969","created_at":"2026-05-18T02:46:13Z"},{"alias_kind":"arxiv_version","alias_value":"1407.7969v1","created_at":"2026-05-18T02:46:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1407.7969","created_at":"2026-05-18T02:46:13Z"},{"alias_kind":"pith_short_12","alias_value":"H6VGKLY5T6PP","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_16","alias_value":"H6VGKLY5T6PPXX7V","created_at":"2026-05-18T12:28:30Z"},{"alias_kind":"pith_short_8","alias_value":"H6VGKLY5","created_at":"2026-05-18T12:28:30Z"}],"graph_snapshots":[{"event_id":"sha256:544fc10444577e2726b678822b33e27e6d364dfcd4f460521d35aeec46fc50e1","target":"graph","created_at":"2026-05-18T02:46:13Z","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 introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious hand-tuning, brute search or lengthy sampling runs. Against this background, Bayesian optimisation is finding increasing use in automating parameter tuning, making ML algorithms accessible even to non-experts. However, the state of the art in Bayesian optimisation is incapable of sca","authors_text":"Michael A Osborne, Stephen J Roberts, Steven Reece, Thomas Nickson","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-07-30T08:29:38Z","title":"Automated Machine Learning on Big Data using Stochastic Algorithm Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1407.7969","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":"sha256:0151b1b0c0a486465d6cfb58834641d3d32b8cb18cc34f379a6ed8d1628a8ef8","target":"record","created_at":"2026-05-18T02:46:13Z","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":"c4fe4e48900bf3d70a41739ec76ed215a8e0e5f6ba481bae18b83ba604e2a13b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-07-30T08:29:38Z","title_canon_sha256":"a6ad30491bc4cecc4e011640512a8552ab5ca511eb92ff170a09e1905fda6b31"},"schema_version":"1.0","source":{"id":"1407.7969","kind":"arxiv","version":1}},"canonical_sha256":"3faa652f1d9f9efbdff55c6a91fcf804c16c52681c20817738746e9e29bf7f4a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3faa652f1d9f9efbdff55c6a91fcf804c16c52681c20817738746e9e29bf7f4a","first_computed_at":"2026-05-18T02:46:13.927311Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:46:13.927311Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0jJxOr92hoXFuU+idGOLjfcrTZBq/dTDYI1oIdQOW859KVYG/odprYaL/m+n8xNluvmAQRqN5kR9jFA8AdXgCA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:46:13.928052Z","signed_message":"canonical_sha256_bytes"},"source_id":"1407.7969","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0151b1b0c0a486465d6cfb58834641d3d32b8cb18cc34f379a6ed8d1628a8ef8","sha256:544fc10444577e2726b678822b33e27e6d364dfcd4f460521d35aeec46fc50e1"],"state_sha256":"1cbb10a409ed467f6ac9d1e732775f8379ba5a79c1e60567012612edd90f9b23"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ob+o9Ph6GG5ns2EPsBY0CRG039mQ+R/FDyx0WdTGQBwC9d+W1qxicBIw6ttXRX7NAoTpYEKcmceq+9O4ugSqDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T07:07:26.588510Z","bundle_sha256":"f2a25ce4df86fc6f206ed2de6f18c0d2c381016e9e860c7a805064da20a6d16e"}}