{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:FCZYAOYMF5CREOWYS3L3YCDCAN","short_pith_number":"pith:FCZYAOYM","schema_version":"1.0","canonical_sha256":"28b3803b0c2f45123ad896d7bc0862037163d54239b4565f95f84a4c17bd737d","source":{"kind":"arxiv","id":"1809.01712","version":3},"attestation_state":"computed","paper":{"title":"Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Spanias, Bhavya Kailkhura, Cihan Tepedelenlioglu, Gowtham Muniraju, Jayaraman J. Thiagarajan, Peer-Timo Bremer","submitted_at":"2018-09-05T19:59:38Z","abstract_excerpt":"Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling and hyper-parameter optimization. Existing solutions attempt to adaptively trade-off between global exploration and local exploitation, wherein the initial exploratory sample is critical to their success. While discrepancy-based samples have become the de facto approach for exploration, results from computer graphics suggest that coverage-based desi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1809.01712","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-05T19:59:38Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"05472298881ec8752bb16112033378dcba6ef52640917b499c69d5cd4ff2086c","abstract_canon_sha256":"cd3f482d5b507790642cba9dd2a11fd8ea618373fca6784aa43eb826b810904a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:22.324473Z","signature_b64":"hPk8cmMBaVKd1U/7VKYc/QWF9UV/9gADLDR3YJE/pWY1RzSljbzk4aURq4X7zSnbP4JE0+c2bE0E4HWeJ+wxBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28b3803b0c2f45123ad896d7bc0862037163d54239b4565f95f84a4c17bd737d","last_reissued_at":"2026-05-17T23:49:22.323884Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:22.323884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Andreas Spanias, Bhavya Kailkhura, Cihan Tepedelenlioglu, Gowtham Muniraju, Jayaraman J. Thiagarajan, Peer-Timo Bremer","submitted_at":"2018-09-05T19:59:38Z","abstract_excerpt":"Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution. Typical examples include data summarization, sample mining for predictive modeling and hyper-parameter optimization. Existing solutions attempt to adaptively trade-off between global exploration and local exploitation, wherein the initial exploratory sample is critical to their success. While discrepancy-based samples have become the de facto approach for exploration, results from computer graphics suggest that coverage-based desi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.01712","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":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1809.01712","created_at":"2026-05-17T23:49:22.323972+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.01712v3","created_at":"2026-05-17T23:49:22.323972+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.01712","created_at":"2026-05-17T23:49:22.323972+00:00"},{"alias_kind":"pith_short_12","alias_value":"FCZYAOYMF5CR","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_16","alias_value":"FCZYAOYMF5CREOWY","created_at":"2026-05-18T12:32:22.470017+00:00"},{"alias_kind":"pith_short_8","alias_value":"FCZYAOYM","created_at":"2026-05-18T12:32:22.470017+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN","json":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN.json","graph_json":"https://pith.science/api/pith-number/FCZYAOYMF5CREOWYS3L3YCDCAN/graph.json","events_json":"https://pith.science/api/pith-number/FCZYAOYMF5CREOWYS3L3YCDCAN/events.json","paper":"https://pith.science/paper/FCZYAOYM"},"agent_actions":{"view_html":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN","download_json":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN.json","view_paper":"https://pith.science/paper/FCZYAOYM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.01712&json=true","fetch_graph":"https://pith.science/api/pith-number/FCZYAOYMF5CREOWYS3L3YCDCAN/graph.json","fetch_events":"https://pith.science/api/pith-number/FCZYAOYMF5CREOWYS3L3YCDCAN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN/action/storage_attestation","attest_author":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN/action/author_attestation","sign_citation":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN/action/citation_signature","submit_replication":"https://pith.science/pith/FCZYAOYMF5CREOWYS3L3YCDCAN/action/replication_record"}},"created_at":"2026-05-17T23:49:22.323972+00:00","updated_at":"2026-05-17T23:49:22.323972+00:00"}