{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:ANV5B7YPBCGZD45J6UAHYWHM2T","short_pith_number":"pith:ANV5B7YP","canonical_record":{"source":{"id":"2606.04866","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T13:32:17Z","cross_cats_sorted":[],"title_canon_sha256":"2395a080c0f38b12d8ef2e9e432c5b20915ba62fb5aa8c056509da939ef42428","abstract_canon_sha256":"4e706ba7e5cd5062ae83d6e3ea0fc5b57bdf1976ea0cb18ca9212c8ad5d31037"},"schema_version":"1.0"},"canonical_sha256":"036bd0ff0f088d91f3a9f5007c58ecd4ed9569caaa6b8168d811aa043578046c","source":{"kind":"arxiv","id":"2606.04866","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04866","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04866v1","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04866","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"ANV5B7YPBCGZ","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"ANV5B7YPBCGZD45J","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"ANV5B7YP","created_at":"2026-06-04T01:09:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:ANV5B7YPBCGZD45J6UAHYWHM2T","target":"record","payload":{"canonical_record":{"source":{"id":"2606.04866","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T13:32:17Z","cross_cats_sorted":[],"title_canon_sha256":"2395a080c0f38b12d8ef2e9e432c5b20915ba62fb5aa8c056509da939ef42428","abstract_canon_sha256":"4e706ba7e5cd5062ae83d6e3ea0fc5b57bdf1976ea0cb18ca9212c8ad5d31037"},"schema_version":"1.0"},"canonical_sha256":"036bd0ff0f088d91f3a9f5007c58ecd4ed9569caaa6b8168d811aa043578046c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-04T01:09:52.189214Z","signature_b64":"1E8igyaSZuJ3sy1LhYfnEmzcr4Lhldt8yooM+BJhb2OM3In0g+LnliyTVD4v7lJjbwLrFK68OWUe1endlSwyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"036bd0ff0f088d91f3a9f5007c58ecd4ed9569caaa6b8168d811aa043578046c","last_reissued_at":"2026-06-04T01:09:52.188499Z","signature_status":"signed_v1","first_computed_at":"2026-06-04T01:09:52.188499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.04866","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":"2026-06-04T01:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eGyUA+ubc4oHNCcwe35prkAT8O2leBP6/tIsyxNEpC1pDxFSn8fOVm4tlVJJS3KKcMLFTJHEi0/lJbrBJqsjBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T12:35:09.737157Z"},"content_sha256":"62a62c09b8e5b2842927d06c08db18279558e10cfba94e113c1718d8b09e4f0e","schema_version":"1.0","event_id":"sha256:62a62c09b8e5b2842927d06c08db18279558e10cfba94e113c1718d8b09e4f0e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:ANV5B7YPBCGZD45J6UAHYWHM2T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Barbara Hammer, Jasmin Brandt, Leona Hennig, Lukas Fehring, Marcel Wever, Marius Lindauer","submitted_at":"2026-06-03T13:32:17Z","abstract_excerpt":"Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and energy efficiency. Existing methods use prior information heuristically to accelerate both black-box and multi-fidelity settings, but they lack a characterization of how prior informativeness quantitatively reduces sample complexity. In this work, we provide the first distribution-dependent sample complexity bounds for multi-fidelity HPO with priors through the formal lens of fixed-budget best-arm identification. By mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04866","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.04866/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":"2026-06-04T01:09:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MXsKtJmnacdAXLG/MMAfzQsizYH/Zibom4ri34Xkzr3SdjCgDsx8VvnPBt2IOwGpxdXRVtlRMf/B2I+LT3ppCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T12:35:09.737754Z"},"content_sha256":"295bd3b331bdd83c250adc25e89f4216cdd9e95cb0c6b4c5722c86140243b3e0","schema_version":"1.0","event_id":"sha256:295bd3b331bdd83c250adc25e89f4216cdd9e95cb0c6b4c5722c86140243b3e0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ANV5B7YPBCGZD45J6UAHYWHM2T/bundle.json","state_url":"https://pith.science/pith/ANV5B7YPBCGZD45J6UAHYWHM2T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ANV5B7YPBCGZD45J6UAHYWHM2T/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":"2026-06-21T12:35:09Z","links":{"resolver":"https://pith.science/pith/ANV5B7YPBCGZD45J6UAHYWHM2T","bundle":"https://pith.science/pith/ANV5B7YPBCGZD45J6UAHYWHM2T/bundle.json","state":"https://pith.science/pith/ANV5B7YPBCGZD45J6UAHYWHM2T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ANV5B7YPBCGZD45J6UAHYWHM2T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ANV5B7YPBCGZD45J6UAHYWHM2T","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":"4e706ba7e5cd5062ae83d6e3ea0fc5b57bdf1976ea0cb18ca9212c8ad5d31037","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T13:32:17Z","title_canon_sha256":"2395a080c0f38b12d8ef2e9e432c5b20915ba62fb5aa8c056509da939ef42428"},"schema_version":"1.0","source":{"id":"2606.04866","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.04866","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"arxiv_version","alias_value":"2606.04866v1","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.04866","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"pith_short_12","alias_value":"ANV5B7YPBCGZ","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"pith_short_16","alias_value":"ANV5B7YPBCGZD45J","created_at":"2026-06-04T01:09:52Z"},{"alias_kind":"pith_short_8","alias_value":"ANV5B7YP","created_at":"2026-06-04T01:09:52Z"}],"graph_snapshots":[{"event_id":"sha256:295bd3b331bdd83c250adc25e89f4216cdd9e95cb0c6b4c5722c86140243b3e0","target":"graph","created_at":"2026-06-04T01:09:52Z","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/2606.04866/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and energy efficiency. Existing methods use prior information heuristically to accelerate both black-box and multi-fidelity settings, but they lack a characterization of how prior informativeness quantitatively reduces sample complexity. In this work, we provide the first distribution-dependent sample complexity bounds for multi-fidelity HPO with priors through the formal lens of fixed-budget best-arm identification. By mo","authors_text":"Barbara Hammer, Jasmin Brandt, Leona Hennig, Lukas Fehring, Marcel Wever, Marius Lindauer","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T13:32:17Z","title":"Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.04866","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":"sha256:62a62c09b8e5b2842927d06c08db18279558e10cfba94e113c1718d8b09e4f0e","target":"record","created_at":"2026-06-04T01:09:52Z","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":"4e706ba7e5cd5062ae83d6e3ea0fc5b57bdf1976ea0cb18ca9212c8ad5d31037","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T13:32:17Z","title_canon_sha256":"2395a080c0f38b12d8ef2e9e432c5b20915ba62fb5aa8c056509da939ef42428"},"schema_version":"1.0","source":{"id":"2606.04866","kind":"arxiv","version":1}},"canonical_sha256":"036bd0ff0f088d91f3a9f5007c58ecd4ed9569caaa6b8168d811aa043578046c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"036bd0ff0f088d91f3a9f5007c58ecd4ed9569caaa6b8168d811aa043578046c","first_computed_at":"2026-06-04T01:09:52.188499Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-04T01:09:52.188499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1E8igyaSZuJ3sy1LhYfnEmzcr4Lhldt8yooM+BJhb2OM3In0g+LnliyTVD4v7lJjbwLrFK68OWUe1endlSwyBA==","signature_status":"signed_v1","signed_at":"2026-06-04T01:09:52.189214Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.04866","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:62a62c09b8e5b2842927d06c08db18279558e10cfba94e113c1718d8b09e4f0e","sha256:295bd3b331bdd83c250adc25e89f4216cdd9e95cb0c6b4c5722c86140243b3e0"],"state_sha256":"9bcf5b4ce9be40507db00dc401061502c64ab166a792b361183c15d48f26326f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1JLCXEyjZJnnNlWYIhHdEpqyNoFx8BqyRogEcqj5i40mVuTpdI5YcM0rzLRNFGXqk+qtVAB9ZRLPGTTcy3DgBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T12:35:09.740432Z","bundle_sha256":"06133aa825064e0e89401b986260cffc898c6eba8c1e5ed771f64e41d1cb7d93"}}