{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TMJ6ALU4FFWJII6VGAQOTEUHAB","short_pith_number":"pith:TMJ6ALU4","canonical_record":{"source":{"id":"2606.10068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-08T18:42:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"49e0e7b060b97ed948cfefc2201d9e6f81b5fb32b880215755fa1215dd914352","abstract_canon_sha256":"5cf2138cb77314cd7dc1b105c05c23cc8ad8b22d8a18e8457c67a5ee5cce226a"},"schema_version":"1.0"},"canonical_sha256":"9b13e02e9c296c9423d53020e99287004071ee0ffac4900ea39fb3aed97ea2c8","source":{"kind":"arxiv","id":"2606.10068","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.10068","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"arxiv_version","alias_value":"2606.10068v1","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10068","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"pith_short_12","alias_value":"TMJ6ALU4FFWJ","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"pith_short_16","alias_value":"TMJ6ALU4FFWJII6V","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"pith_short_8","alias_value":"TMJ6ALU4","created_at":"2026-06-10T00:08:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TMJ6ALU4FFWJII6VGAQOTEUHAB","target":"record","payload":{"canonical_record":{"source":{"id":"2606.10068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-08T18:42:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"49e0e7b060b97ed948cfefc2201d9e6f81b5fb32b880215755fa1215dd914352","abstract_canon_sha256":"5cf2138cb77314cd7dc1b105c05c23cc8ad8b22d8a18e8457c67a5ee5cce226a"},"schema_version":"1.0"},"canonical_sha256":"9b13e02e9c296c9423d53020e99287004071ee0ffac4900ea39fb3aed97ea2c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T00:08:47.394687Z","signature_b64":"oa3eu6tS+1Nyf/ewWDQ+Su2jvWgERBsNOfl3pNHGDs/Io1lft4Y60lCfBsiKoF+0fH2mvwsdY6uLB8BTsRIHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b13e02e9c296c9423d53020e99287004071ee0ffac4900ea39fb3aed97ea2c8","last_reissued_at":"2026-06-10T00:08:47.393752Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T00:08:47.393752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.10068","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":"2026-06-10T00:08:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NMCej//ciivpteQ0WCZ6K8AyZv79X7ekk8nH+x7VEQpGKLvndTsO95hm5/GaXF5hOPs1rUwt1TS9q88b+RY7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T21:01:19.236861Z"},"content_sha256":"700ec611db5d61e22c606d84515035c4c9f9033cd68abe3d5c6b114264265a21","schema_version":"1.0","event_id":"sha256:700ec611db5d61e22c606d84515035c4c9f9033cd68abe3d5c6b114264265a21"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TMJ6ALU4FFWJII6VGAQOTEUHAB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ian Nabney, Mohammad Golbabaee, Ruinan Wang","submitted_at":"2026-06-08T18:42:00Z","abstract_excerpt":"Hyperparameter Optimization (HPO) is essential for building high-performing ML/DL models, yet conventional optimizers often struggle in high-dimensional spaces where evaluations are costly and progress is diluted across many low-impact variables. We propose Greedy Importance First (GIF), an importance-aware scheduling strategy that uses a small-sample warm start to estimate hyperparameter importance, forms importance-based groups, allocates trials proportionally, and retains a full-space fallback. We evaluate GIF under fixed evaluation budgets on five anisotropic analytic functions, Bayesmark,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10068","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/2606.10068/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":"2026-06-10T00:08:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zBg73Os4g3Eatjzbyf+fV3Tzew9hs0/b6cTVrX1fGj+ekCo+bfSKV8167oqSbhTCLMjuqD8RSWNttqxjcN+BCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-21T21:01:19.237241Z"},"content_sha256":"f1f2a1baf33737fab221d885c88129e552338d2c180383ccb4291cd3bac812b9","schema_version":"1.0","event_id":"sha256:f1f2a1baf33737fab221d885c88129e552338d2c180383ccb4291cd3bac812b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TMJ6ALU4FFWJII6VGAQOTEUHAB/bundle.json","state_url":"https://pith.science/pith/TMJ6ALU4FFWJII6VGAQOTEUHAB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TMJ6ALU4FFWJII6VGAQOTEUHAB/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":"2026-06-21T21:01:19Z","links":{"resolver":"https://pith.science/pith/TMJ6ALU4FFWJII6VGAQOTEUHAB","bundle":"https://pith.science/pith/TMJ6ALU4FFWJII6VGAQOTEUHAB/bundle.json","state":"https://pith.science/pith/TMJ6ALU4FFWJII6VGAQOTEUHAB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TMJ6ALU4FFWJII6VGAQOTEUHAB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TMJ6ALU4FFWJII6VGAQOTEUHAB","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":"5cf2138cb77314cd7dc1b105c05c23cc8ad8b22d8a18e8457c67a5ee5cce226a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-08T18:42:00Z","title_canon_sha256":"49e0e7b060b97ed948cfefc2201d9e6f81b5fb32b880215755fa1215dd914352"},"schema_version":"1.0","source":{"id":"2606.10068","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.10068","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"arxiv_version","alias_value":"2606.10068v1","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10068","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"pith_short_12","alias_value":"TMJ6ALU4FFWJ","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"pith_short_16","alias_value":"TMJ6ALU4FFWJII6V","created_at":"2026-06-10T00:08:47Z"},{"alias_kind":"pith_short_8","alias_value":"TMJ6ALU4","created_at":"2026-06-10T00:08:47Z"}],"graph_snapshots":[{"event_id":"sha256:f1f2a1baf33737fab221d885c88129e552338d2c180383ccb4291cd3bac812b9","target":"graph","created_at":"2026-06-10T00:08:47Z","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/2606.10068/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Hyperparameter Optimization (HPO) is essential for building high-performing ML/DL models, yet conventional optimizers often struggle in high-dimensional spaces where evaluations are costly and progress is diluted across many low-impact variables. We propose Greedy Importance First (GIF), an importance-aware scheduling strategy that uses a small-sample warm start to estimate hyperparameter importance, forms importance-based groups, allocates trials proportionally, and retains a full-space fallback. We evaluate GIF under fixed evaluation budgets on five anisotropic analytic functions, Bayesmark,","authors_text":"Ian Nabney, Mohammad Golbabaee, Ruinan Wang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-08T18:42:00Z","title":"Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10068","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":"sha256:700ec611db5d61e22c606d84515035c4c9f9033cd68abe3d5c6b114264265a21","target":"record","created_at":"2026-06-10T00:08:47Z","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":"5cf2138cb77314cd7dc1b105c05c23cc8ad8b22d8a18e8457c67a5ee5cce226a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-08T18:42:00Z","title_canon_sha256":"49e0e7b060b97ed948cfefc2201d9e6f81b5fb32b880215755fa1215dd914352"},"schema_version":"1.0","source":{"id":"2606.10068","kind":"arxiv","version":1}},"canonical_sha256":"9b13e02e9c296c9423d53020e99287004071ee0ffac4900ea39fb3aed97ea2c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b13e02e9c296c9423d53020e99287004071ee0ffac4900ea39fb3aed97ea2c8","first_computed_at":"2026-06-10T00:08:47.393752Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T00:08:47.393752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oa3eu6tS+1Nyf/ewWDQ+Su2jvWgERBsNOfl3pNHGDs/Io1lft4Y60lCfBsiKoF+0fH2mvwsdY6uLB8BTsRIHAg==","signature_status":"signed_v1","signed_at":"2026-06-10T00:08:47.394687Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.10068","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:700ec611db5d61e22c606d84515035c4c9f9033cd68abe3d5c6b114264265a21","sha256:f1f2a1baf33737fab221d885c88129e552338d2c180383ccb4291cd3bac812b9"],"state_sha256":"3fd6458732ddfde1a90e71a1991d60d588e2f3cb4fbda339c45e49c6075720cc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wnHe6oPqIv4hq69sRVjxwnHLn7YrIQrfR7nesksyPEc6HUv2x2rnz5RSstVy0CZ6fXp7aw8h6BdWHQi07dJzAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-21T21:01:19.239296Z","bundle_sha256":"4085fb6972b3c551056e40d9abcffa5f6a7471dc8163b4a23ec57973dd525c3c"}}