{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:N6BI6NJZBK55FSHRUIAM2GJBDF","short_pith_number":"pith:N6BI6NJZ","canonical_record":{"source":{"id":"1703.03373","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-09T17:52:50Z","cross_cats_sorted":[],"title_canon_sha256":"f9f852ad6cb696073887f4628dfbeda96adf724718ae0e5fc7e48d001c7d6941","abstract_canon_sha256":"d99f0644d0cb2254c8453c68a1c2efb28fa7fae2275f56b426b02c5c39ccdd7d"},"schema_version":"1.0"},"canonical_sha256":"6f828f35390abbd2c8f1a200cd1921197c26a4c5a8d291eb945e92c8e1be0976","source":{"kind":"arxiv","id":"1703.03373","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.03373","created_at":"2026-05-17T23:59:25Z"},{"alias_kind":"arxiv_version","alias_value":"1703.03373v3","created_at":"2026-05-17T23:59:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03373","created_at":"2026-05-17T23:59:25Z"},{"alias_kind":"pith_short_12","alias_value":"N6BI6NJZBK55","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N6BI6NJZBK55FSHR","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N6BI6NJZ","created_at":"2026-05-18T12:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:N6BI6NJZBK55FSHRUIAM2GJBDF","target":"record","payload":{"canonical_record":{"source":{"id":"1703.03373","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-09T17:52:50Z","cross_cats_sorted":[],"title_canon_sha256":"f9f852ad6cb696073887f4628dfbeda96adf724718ae0e5fc7e48d001c7d6941","abstract_canon_sha256":"d99f0644d0cb2254c8453c68a1c2efb28fa7fae2275f56b426b02c5c39ccdd7d"},"schema_version":"1.0"},"canonical_sha256":"6f828f35390abbd2c8f1a200cd1921197c26a4c5a8d291eb945e92c8e1be0976","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:25.352619Z","signature_b64":"QpXRj271+W16V1Pn9LFaIR6dfKKE9/8XCe3XWSsRsshQx/G5sCd5XiV6zTLgx3c2qegnlKet+99d4ngz4KkYAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f828f35390abbd2c8f1a200cd1921197c26a4c5a8d291eb945e92c8e1be0976","last_reissued_at":"2026-05-17T23:59:25.351904Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:25.351904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.03373","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":"2026-05-17T23:59:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xJVFg6ozIS8kiBDuAyvByX8iGOt2XgOjEYln/AnV3dDIlM+LJ1YPGVhTVgRHGMIm6DRo2PV9p0SSILNjWFUiCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T01:04:52.425270Z"},"content_sha256":"6132c63dcccbcf60e711cd4e6b2c3c6a647fff21adddf14c91cdebffbc4cebff","schema_version":"1.0","event_id":"sha256:6132c63dcccbcf60e711cd4e6b2c3c6a647fff21adddf14c91cdebffbc4cebff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:N6BI6NJZBK55FSHRUIAM2GJBDF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Bernd Bischl, Daniel Horn, Jakob Bossek, Jakob Richter, Janek Thomas, Michel Lang","submitted_at":"2017-03-09T17:52:50Z","abstract_excerpt":"We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi-objective optimization with mixed continuous, categorical and conditional parameters. Additional features include multi-point batch proposal, parallelization, visualization, logging and error-handling. mlrMBO is implemented in a modular fashion, such that single components can b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03373","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":"2026-05-17T23:59:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aqXFYmGwzkPd0NsKhIamM7ewa4gHx2fKXhPfdtJ+2ukATBS6E9Os1mAVyQu031k9wlwTAgcvv23Dm1mes30bCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T01:04:52.425915Z"},"content_sha256":"97e170317eb0475beaeb9e405a4f7b32cdb8f52f3fcdddf78330a3ad2e0864b2","schema_version":"1.0","event_id":"sha256:97e170317eb0475beaeb9e405a4f7b32cdb8f52f3fcdddf78330a3ad2e0864b2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N6BI6NJZBK55FSHRUIAM2GJBDF/bundle.json","state_url":"https://pith.science/pith/N6BI6NJZBK55FSHRUIAM2GJBDF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N6BI6NJZBK55FSHRUIAM2GJBDF/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":"2026-06-01T01:04:52Z","links":{"resolver":"https://pith.science/pith/N6BI6NJZBK55FSHRUIAM2GJBDF","bundle":"https://pith.science/pith/N6BI6NJZBK55FSHRUIAM2GJBDF/bundle.json","state":"https://pith.science/pith/N6BI6NJZBK55FSHRUIAM2GJBDF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N6BI6NJZBK55FSHRUIAM2GJBDF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:N6BI6NJZBK55FSHRUIAM2GJBDF","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":"d99f0644d0cb2254c8453c68a1c2efb28fa7fae2275f56b426b02c5c39ccdd7d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-09T17:52:50Z","title_canon_sha256":"f9f852ad6cb696073887f4628dfbeda96adf724718ae0e5fc7e48d001c7d6941"},"schema_version":"1.0","source":{"id":"1703.03373","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.03373","created_at":"2026-05-17T23:59:25Z"},{"alias_kind":"arxiv_version","alias_value":"1703.03373v3","created_at":"2026-05-17T23:59:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03373","created_at":"2026-05-17T23:59:25Z"},{"alias_kind":"pith_short_12","alias_value":"N6BI6NJZBK55","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"N6BI6NJZBK55FSHR","created_at":"2026-05-18T12:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"N6BI6NJZ","created_at":"2026-05-18T12:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:97e170317eb0475beaeb9e405a4f7b32cdb8f52f3fcdddf78330a3ad2e0864b2","target":"graph","created_at":"2026-05-17T23:59:25Z","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 present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi-objective optimization with mixed continuous, categorical and conditional parameters. Additional features include multi-point batch proposal, parallelization, visualization, logging and error-handling. mlrMBO is implemented in a modular fashion, such that single components can b","authors_text":"Bernd Bischl, Daniel Horn, Jakob Bossek, Jakob Richter, Janek Thomas, Michel Lang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-09T17:52:50Z","title":"mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03373","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":"sha256:6132c63dcccbcf60e711cd4e6b2c3c6a647fff21adddf14c91cdebffbc4cebff","target":"record","created_at":"2026-05-17T23:59:25Z","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":"d99f0644d0cb2254c8453c68a1c2efb28fa7fae2275f56b426b02c5c39ccdd7d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-03-09T17:52:50Z","title_canon_sha256":"f9f852ad6cb696073887f4628dfbeda96adf724718ae0e5fc7e48d001c7d6941"},"schema_version":"1.0","source":{"id":"1703.03373","kind":"arxiv","version":3}},"canonical_sha256":"6f828f35390abbd2c8f1a200cd1921197c26a4c5a8d291eb945e92c8e1be0976","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f828f35390abbd2c8f1a200cd1921197c26a4c5a8d291eb945e92c8e1be0976","first_computed_at":"2026-05-17T23:59:25.351904Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:25.351904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QpXRj271+W16V1Pn9LFaIR6dfKKE9/8XCe3XWSsRsshQx/G5sCd5XiV6zTLgx3c2qegnlKet+99d4ngz4KkYAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:25.352619Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.03373","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6132c63dcccbcf60e711cd4e6b2c3c6a647fff21adddf14c91cdebffbc4cebff","sha256:97e170317eb0475beaeb9e405a4f7b32cdb8f52f3fcdddf78330a3ad2e0864b2"],"state_sha256":"b8f498da7943a430ac554da25dd835d7cf8e875906e53b43461c657776d8a630"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pRQ7yZ69/dRf5XzSF6koukIGZT7pJW6ytLyJn0l8Z4mqy2CSuBoL/h3oBgTJPNNMvM006LMQCv6tO4GVzt66AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T01:04:52.428955Z","bundle_sha256":"43e23fa5140ac7003b86c6a70baccd54b4c7a9d7e67591f5b7420c15d9a3ec10"}}