{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QXDA3PVYB6ANSIRHOBSNYHH2W7","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":"e873d24a1451fa77b19f5d43a0bf6e639716c18079c77422af17ecbee91ced71","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-28T00:30:07Z","title_canon_sha256":"cc388ad13895be967043490811bf52361d354d97bc8bd286da4a8cb9d61c2676"},"schema_version":"1.0","source":{"id":"2507.20446","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.20446","created_at":"2026-07-05T11:50:21Z"},{"alias_kind":"arxiv_version","alias_value":"2507.20446v2","created_at":"2026-07-05T11:50:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.20446","created_at":"2026-07-05T11:50:21Z"},{"alias_kind":"pith_short_12","alias_value":"QXDA3PVYB6AN","created_at":"2026-07-05T11:50:21Z"},{"alias_kind":"pith_short_16","alias_value":"QXDA3PVYB6ANSIRH","created_at":"2026-07-05T11:50:21Z"},{"alias_kind":"pith_short_8","alias_value":"QXDA3PVY","created_at":"2026-07-05T11:50:21Z"}],"graph_snapshots":[{"event_id":"sha256:91427fcb78cf0319bb2fdc17b5375490d054efce8cabf4101a80710230bcc485","target":"graph","created_at":"2026-07-05T11:50:21Z","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/2507.20446/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning task successfully and efficiently. Finding the optimal machine learning model or the hyperparameter combination set from a large number of possible alternatives usually requires considerable expert knowledge and experience. To tackle this problem, we propose a combined Bayesian Optimization and Adaptive Successive Filtering algorithm (BOASF) under a unified multi-armed bandit framework to automate the model selection or","authors_text":"Chunfeng Yuan, Feng Cheng, Guanghui Zhu, Lei Wang, Wenzhong Chen, Xin Fang, Yihua Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-28T00:30:07Z","title":"BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.20446","kind":"arxiv","version":2},"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:ea5250443b513dbc3f383c0b02e974f23daaa05217c8b1f67147845325e5ab50","target":"record","created_at":"2026-07-05T11:50:21Z","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":"e873d24a1451fa77b19f5d43a0bf6e639716c18079c77422af17ecbee91ced71","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-28T00:30:07Z","title_canon_sha256":"cc388ad13895be967043490811bf52361d354d97bc8bd286da4a8cb9d61c2676"},"schema_version":"1.0","source":{"id":"2507.20446","kind":"arxiv","version":2}},"canonical_sha256":"85c60dbeb80f80d922277064dc1cfab7c29e5199d1cc14539994675487acc010","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"85c60dbeb80f80d922277064dc1cfab7c29e5199d1cc14539994675487acc010","first_computed_at":"2026-07-05T11:50:21.129516Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:50:21.129516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AFHllGVdqBVGeBiEfve99eiWHJPqUPvLIUEgXZcR4kS5JX/zmav0HNUXdriKwVeG8oBiGSb1UzMb6ngOLOV7Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:50:21.130038Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.20446","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea5250443b513dbc3f383c0b02e974f23daaa05217c8b1f67147845325e5ab50","sha256:91427fcb78cf0319bb2fdc17b5375490d054efce8cabf4101a80710230bcc485"],"state_sha256":"6c7382d9b15d5242f78a18cf4fcb9a09221e89b0028609da2d8d1633aeaf7868"}