{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ADHLAX4YV4EUWFNGAGLEQL77J4","short_pith_number":"pith:ADHLAX4Y","schema_version":"1.0","canonical_sha256":"00ceb05f98af094b15a60196482fff4f047a8ff262c0e89a3e29155446bf0218","source":{"kind":"arxiv","id":"2010.13117","version":1},"attestation_state":"computed","paper":{"title":"Hyperparameter Transfer Across Developer Adjustments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Danny Stoll, Diane Wagner, Frank Hutter, J\\\"org K.H. Franke, Simon Selg","submitted_at":"2020-10-25T13:35:37Z","abstract_excerpt":"After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can change which hyperparameter settings perform well, or even the hyperparameter search space itself. While many approaches exist that leverage knowledge obtained on previous tasks, so far, knowledge from previous development steps remains entirely untapped. In this work, we remedy this situation and propose a new research framework: hyperparameter transf"},"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":"2010.13117","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-25T13:35:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7a6e2dba4727d318c3b38a34f791087928daf3ac660c1aa975563ff27e330ffe","abstract_canon_sha256":"465a25edbca4e58a452bf483c39b91707614b096d076cb0a5357395fa2832d26"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:45:52.116474Z","signature_b64":"Wv3NNYGDO5N1/pf8eL6rJNCnlUYYAWhGl8My88ORoZgbcH5X8B73MmX9Y8Ol+EssEdSIyJrwLNjwd6g46by/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00ceb05f98af094b15a60196482fff4f047a8ff262c0e89a3e29155446bf0218","last_reissued_at":"2026-07-05T01:45:52.116124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:45:52.116124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hyperparameter Transfer Across Developer Adjustments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Danny Stoll, Diane Wagner, Frank Hutter, J\\\"org K.H. Franke, Simon Selg","submitted_at":"2020-10-25T13:35:37Z","abstract_excerpt":"After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can change which hyperparameter settings perform well, or even the hyperparameter search space itself. While many approaches exist that leverage knowledge obtained on previous tasks, so far, knowledge from previous development steps remains entirely untapped. In this work, we remedy this situation and propose a new research framework: hyperparameter transf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.13117","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/2010.13117/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2010.13117","created_at":"2026-07-05T01:45:52.116180+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.13117v1","created_at":"2026-07-05T01:45:52.116180+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.13117","created_at":"2026-07-05T01:45:52.116180+00:00"},{"alias_kind":"pith_short_12","alias_value":"ADHLAX4YV4EU","created_at":"2026-07-05T01:45:52.116180+00:00"},{"alias_kind":"pith_short_16","alias_value":"ADHLAX4YV4EUWFNG","created_at":"2026-07-05T01:45:52.116180+00:00"},{"alias_kind":"pith_short_8","alias_value":"ADHLAX4Y","created_at":"2026-07-05T01:45:52.116180+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/ADHLAX4YV4EUWFNGAGLEQL77J4","json":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4.json","graph_json":"https://pith.science/api/pith-number/ADHLAX4YV4EUWFNGAGLEQL77J4/graph.json","events_json":"https://pith.science/api/pith-number/ADHLAX4YV4EUWFNGAGLEQL77J4/events.json","paper":"https://pith.science/paper/ADHLAX4Y"},"agent_actions":{"view_html":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4","download_json":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4.json","view_paper":"https://pith.science/paper/ADHLAX4Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.13117&json=true","fetch_graph":"https://pith.science/api/pith-number/ADHLAX4YV4EUWFNGAGLEQL77J4/graph.json","fetch_events":"https://pith.science/api/pith-number/ADHLAX4YV4EUWFNGAGLEQL77J4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4/action/storage_attestation","attest_author":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4/action/author_attestation","sign_citation":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4/action/citation_signature","submit_replication":"https://pith.science/pith/ADHLAX4YV4EUWFNGAGLEQL77J4/action/replication_record"}},"created_at":"2026-07-05T01:45:52.116180+00:00","updated_at":"2026-07-05T01:45:52.116180+00:00"}