{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RUT3KKTNL7O5KLMM3FAHQWBKFW","short_pith_number":"pith:RUT3KKTN","schema_version":"1.0","canonical_sha256":"8d27b52a6d5fddd52d8cd94078582a2dbcb86d51d878dcf32fd4307d9f475a9b","source":{"kind":"arxiv","id":"2501.11414","version":1},"attestation_state":"computed","paper":{"title":"Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Emma Hart, Quentin Renau","submitted_at":"2025-01-20T11:28:45Z","abstract_excerpt":"Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is algorithm-centric in order to encapsulate information about how an algorithm performs on an instance, rather than relying on information derived from features of the instance itself. Probing-trajectories that consist of a sequence of objective performance per function evaluation obtained from a short run of an algorithm have recently shown particular promise in training accurate selectors. However, training models on this type of data requires an appropriat"},"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":"2501.11414","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-20T11:28:45Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"a22ce44ca6c15e225530c90c0ded8d06e96fb106c8c92d79f702035f3b7f88a7","abstract_canon_sha256":"3dfa4d56dc747136aebd21319cd2f487953f5c0a032dce8b8722da021535caa9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:03:08.365451Z","signature_b64":"OjGzQqWxhpMpyACo6ByI9pWVQomD2AvsdkoORwU9Jgl7zWmqreQmx74O+QEgwhPwz/1CFVl2stRhrz10bwVRCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d27b52a6d5fddd52d8cd94078582a2dbcb86d51d878dcf32fd4307d9f475a9b","last_reissued_at":"2026-07-05T10:03:08.364986Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:03:08.364986Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"Emma Hart, Quentin Renau","submitted_at":"2025-01-20T11:28:45Z","abstract_excerpt":"Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is algorithm-centric in order to encapsulate information about how an algorithm performs on an instance, rather than relying on information derived from features of the instance itself. Probing-trajectories that consist of a sequence of objective performance per function evaluation obtained from a short run of an algorithm have recently shown particular promise in training accurate selectors. However, training models on this type of data requires an appropriat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.11414","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/2501.11414/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":"2501.11414","created_at":"2026-07-05T10:03:08.365041+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.11414v1","created_at":"2026-07-05T10:03:08.365041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.11414","created_at":"2026-07-05T10:03:08.365041+00:00"},{"alias_kind":"pith_short_12","alias_value":"RUT3KKTNL7O5","created_at":"2026-07-05T10:03:08.365041+00:00"},{"alias_kind":"pith_short_16","alias_value":"RUT3KKTNL7O5KLMM","created_at":"2026-07-05T10:03:08.365041+00:00"},{"alias_kind":"pith_short_8","alias_value":"RUT3KKTN","created_at":"2026-07-05T10:03:08.365041+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/RUT3KKTNL7O5KLMM3FAHQWBKFW","json":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW.json","graph_json":"https://pith.science/api/pith-number/RUT3KKTNL7O5KLMM3FAHQWBKFW/graph.json","events_json":"https://pith.science/api/pith-number/RUT3KKTNL7O5KLMM3FAHQWBKFW/events.json","paper":"https://pith.science/paper/RUT3KKTN"},"agent_actions":{"view_html":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW","download_json":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW.json","view_paper":"https://pith.science/paper/RUT3KKTN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.11414&json=true","fetch_graph":"https://pith.science/api/pith-number/RUT3KKTNL7O5KLMM3FAHQWBKFW/graph.json","fetch_events":"https://pith.science/api/pith-number/RUT3KKTNL7O5KLMM3FAHQWBKFW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW/action/storage_attestation","attest_author":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW/action/author_attestation","sign_citation":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW/action/citation_signature","submit_replication":"https://pith.science/pith/RUT3KKTNL7O5KLMM3FAHQWBKFW/action/replication_record"}},"created_at":"2026-07-05T10:03:08.365041+00:00","updated_at":"2026-07-05T10:03:08.365041+00:00"}