{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:QSGPXNLEPIL2LKFSYYIDZZZULU","short_pith_number":"pith:QSGPXNLE","canonical_record":{"source":{"id":"2206.12708","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-25T18:44:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9ca59ac2a322554ec79adc0f16bf96343b69c071b4fb26718667820324e9d36c","abstract_canon_sha256":"f6275fd26d2bb324cd5ba52dba51ed0affe43d96de932a22098e5baf8a7f6db8"},"schema_version":"1.0"},"canonical_sha256":"848cfbb5647a17a5a8b2c6103ce7345d17b644301f3f96394cdbfd36eb9e34c3","source":{"kind":"arxiv","id":"2206.12708","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.12708","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"arxiv_version","alias_value":"2206.12708v3","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.12708","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"pith_short_12","alias_value":"QSGPXNLEPIL2","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"pith_short_16","alias_value":"QSGPXNLEPIL2LKFS","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"pith_short_8","alias_value":"QSGPXNLE","created_at":"2026-07-05T05:45:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:QSGPXNLEPIL2LKFSYYIDZZZULU","target":"record","payload":{"canonical_record":{"source":{"id":"2206.12708","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-25T18:44:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9ca59ac2a322554ec79adc0f16bf96343b69c071b4fb26718667820324e9d36c","abstract_canon_sha256":"f6275fd26d2bb324cd5ba52dba51ed0affe43d96de932a22098e5baf8a7f6db8"},"schema_version":"1.0"},"canonical_sha256":"848cfbb5647a17a5a8b2c6103ce7345d17b644301f3f96394cdbfd36eb9e34c3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:45:30.184059Z","signature_b64":"Sh3CmXUUCoTaktlCJkk6EOvB8VzkIOe+YKcIKB+Q/jQlBDjl1k2HXF9mgCi7xdErCiPr6Pzu4KDz/naKDVPvBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"848cfbb5647a17a5a8b2c6103ce7345d17b644301f3f96394cdbfd36eb9e34c3","last_reissued_at":"2026-07-05T05:45:30.183714Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:45:30.183714Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.12708","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":"2026-07-05T05:45:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZVnEUctXKRrEk0S3kBWSPT8wRY+ChVewvy/kGtMpc9WsUwfA/MJYguNDwXesh4LgIyRdFLj+k8MTuovH3YvkDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T13:47:08.249610Z"},"content_sha256":"2b6068a2d9d265efc107d430e4e67bf4c58553a82e90e0a04977a3f455fe63c3","schema_version":"1.0","event_id":"sha256:2b6068a2d9d265efc107d430e4e67bf4c58553a82e90e0a04977a3f455fe63c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:QSGPXNLEPIL2LKFSYYIDZZZULU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Janardhan Rao Doppa, Nicolo Fusi, Rishit Sheth, Syrine Belakaria","submitted_at":"2022-06-25T18:44:06Z","abstract_excerpt":"The rising growth of deep neural networks (DNNs) and datasets in size motivates the need for efficient solutions for simultaneous model selection and training. Many methods for hyperparameter optimization (HPO) of iterative learners, including DNNs, attempt to solve this problem by querying and learning a response surface while searching for the optimum of that surface. However, many of these methods make myopic queries, do not consider prior knowledge about the response structure, and/or perform a biased cost-aware search, all of which exacerbate identifying the best-performing model when a t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.12708","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.12708/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":"2026-07-05T05:45:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ocz+llXldUZK53PI3l0P5bZVmlremsbHdEc43nPYkg9BsAtyoG6Gf/5yWZf8DdF4otlPIVz0LVfa0ers1JTTAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T13:47:08.250118Z"},"content_sha256":"2841628f08f9d9fb0170b7f25ba01a71653dc8f39d2c5e102133beede4cb4596","schema_version":"1.0","event_id":"sha256:2841628f08f9d9fb0170b7f25ba01a71653dc8f39d2c5e102133beede4cb4596"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QSGPXNLEPIL2LKFSYYIDZZZULU/bundle.json","state_url":"https://pith.science/pith/QSGPXNLEPIL2LKFSYYIDZZZULU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QSGPXNLEPIL2LKFSYYIDZZZULU/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":"2026-08-12T13:47:08Z","links":{"resolver":"https://pith.science/pith/QSGPXNLEPIL2LKFSYYIDZZZULU","bundle":"https://pith.science/pith/QSGPXNLEPIL2LKFSYYIDZZZULU/bundle.json","state":"https://pith.science/pith/QSGPXNLEPIL2LKFSYYIDZZZULU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QSGPXNLEPIL2LKFSYYIDZZZULU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:QSGPXNLEPIL2LKFSYYIDZZZULU","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":"f6275fd26d2bb324cd5ba52dba51ed0affe43d96de932a22098e5baf8a7f6db8","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-25T18:44:06Z","title_canon_sha256":"9ca59ac2a322554ec79adc0f16bf96343b69c071b4fb26718667820324e9d36c"},"schema_version":"1.0","source":{"id":"2206.12708","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.12708","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"arxiv_version","alias_value":"2206.12708v3","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.12708","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"pith_short_12","alias_value":"QSGPXNLEPIL2","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"pith_short_16","alias_value":"QSGPXNLEPIL2LKFS","created_at":"2026-07-05T05:45:30Z"},{"alias_kind":"pith_short_8","alias_value":"QSGPXNLE","created_at":"2026-07-05T05:45:30Z"}],"graph_snapshots":[{"event_id":"sha256:2841628f08f9d9fb0170b7f25ba01a71653dc8f39d2c5e102133beede4cb4596","target":"graph","created_at":"2026-07-05T05:45:30Z","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/2206.12708/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rising growth of deep neural networks (DNNs) and datasets in size motivates the need for efficient solutions for simultaneous model selection and training. Many methods for hyperparameter optimization (HPO) of iterative learners, including DNNs, attempt to solve this problem by querying and learning a response surface while searching for the optimum of that surface. However, many of these methods make myopic queries, do not consider prior knowledge about the response structure, and/or perform a biased cost-aware search, all of which exacerbate identifying the best-performing model when a t","authors_text":"Janardhan Rao Doppa, Nicolo Fusi, Rishit Sheth, Syrine Belakaria","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-25T18:44:06Z","title":"Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.12708","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":"sha256:2b6068a2d9d265efc107d430e4e67bf4c58553a82e90e0a04977a3f455fe63c3","target":"record","created_at":"2026-07-05T05:45:30Z","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":"f6275fd26d2bb324cd5ba52dba51ed0affe43d96de932a22098e5baf8a7f6db8","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-25T18:44:06Z","title_canon_sha256":"9ca59ac2a322554ec79adc0f16bf96343b69c071b4fb26718667820324e9d36c"},"schema_version":"1.0","source":{"id":"2206.12708","kind":"arxiv","version":3}},"canonical_sha256":"848cfbb5647a17a5a8b2c6103ce7345d17b644301f3f96394cdbfd36eb9e34c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"848cfbb5647a17a5a8b2c6103ce7345d17b644301f3f96394cdbfd36eb9e34c3","first_computed_at":"2026-07-05T05:45:30.183714Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:45:30.183714Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Sh3CmXUUCoTaktlCJkk6EOvB8VzkIOe+YKcIKB+Q/jQlBDjl1k2HXF9mgCi7xdErCiPr6Pzu4KDz/naKDVPvBg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:45:30.184059Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.12708","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b6068a2d9d265efc107d430e4e67bf4c58553a82e90e0a04977a3f455fe63c3","sha256:2841628f08f9d9fb0170b7f25ba01a71653dc8f39d2c5e102133beede4cb4596"],"state_sha256":"3601b81cd40060c682684cd25e396f92ca4acb2022310a119a8d8e123b8570e9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HIduXGxPkDl6VuVG2W6TH+aIwmyePSfSfigpIbVW2Fbn5ZpCXoSOB4iPAwijjtc7zbIMTYiYe05Zu6t7DEy7Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T13:47:08.254273Z","bundle_sha256":"687d8e7509762505e276359661a615c1b6267da6fc963db3400035f2910dcd72"}}