{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ELUVWA3M25BEUTKAUQLDRZBRH4","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":"7bae6f248851d2db53a535056fdd0b1b9ff54b02be60e46885a19ecac852ad00","cross_cats_sorted":["cs.DC","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-14T01:31:45Z","title_canon_sha256":"8140a44094d7cbb68303428906d736cd7d6303fffe6ff730608aa66ac1c314d6"},"schema_version":"1.0","source":{"id":"1908.04909","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.04909","created_at":"2026-07-04T23:55:57Z"},{"alias_kind":"arxiv_version","alias_value":"1908.04909v1","created_at":"2026-07-04T23:55:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.04909","created_at":"2026-07-04T23:55:57Z"},{"alias_kind":"pith_short_12","alias_value":"ELUVWA3M25BE","created_at":"2026-07-04T23:55:57Z"},{"alias_kind":"pith_short_16","alias_value":"ELUVWA3M25BEUTKA","created_at":"2026-07-04T23:55:57Z"},{"alias_kind":"pith_short_8","alias_value":"ELUVWA3M","created_at":"2026-07-04T23:55:57Z"}],"graph_snapshots":[{"event_id":"sha256:3e274bf10e5f7b09d4985f5205013d1bd172ca1bee2171de1df240083c46aded","target":"graph","created_at":"2026-07-04T23:55:57Z","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/1908.04909/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General purpose machine learning software that simultaneously supports multiple objectives and constraints is scant, though the potential benefits are great. In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems. Autotune is built on a suite of derivative-free optimization methods, and utilizes multi-level parallelism in a di","authors_text":"Brett Wujek, Joshua Griffin, Oleg Golovidov, Patrick Koch, Steven Gardner, Wayne Thompson, Yan Xu","cross_cats":["cs.DC","cs.NE","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-14T01:31:45Z","title":"Constrained Multi-Objective Optimization for Automated Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.04909","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":"sha256:0c39d36ae54d983160dd203f67f04afee4ae6ff66be4dd6d79ee5d09cf3c015b","target":"record","created_at":"2026-07-04T23:55:57Z","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":"7bae6f248851d2db53a535056fdd0b1b9ff54b02be60e46885a19ecac852ad00","cross_cats_sorted":["cs.DC","cs.NE","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-08-14T01:31:45Z","title_canon_sha256":"8140a44094d7cbb68303428906d736cd7d6303fffe6ff730608aa66ac1c314d6"},"schema_version":"1.0","source":{"id":"1908.04909","kind":"arxiv","version":1}},"canonical_sha256":"22e95b036cd7424a4d40a41638e4313f07670fc7e2844c7c21aef2e0251717fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"22e95b036cd7424a4d40a41638e4313f07670fc7e2844c7c21aef2e0251717fd","first_computed_at":"2026-07-04T23:55:57.906842Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:55:57.906842Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KLkngvlWCAKxwO+LHfmlIAHrfcfRxT4H0WHQXuosrtQImuepKPK/gAK4phlXx0OMKuAq5VhDbLK0tJThsHQ3Dw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:55:57.907260Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.04909","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0c39d36ae54d983160dd203f67f04afee4ae6ff66be4dd6d79ee5d09cf3c015b","sha256:3e274bf10e5f7b09d4985f5205013d1bd172ca1bee2171de1df240083c46aded"],"state_sha256":"bf3bdd6b353e45b194f967175a36581ed4d6909c5ad9a6ac6a0afd54039cc909"}