{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:G3XATSHQXAVVBZXWHFMO5PL54X","short_pith_number":"pith:G3XATSHQ","schema_version":"1.0","canonical_sha256":"36ee09c8f0b82b50e6f63958eebd7de5d005e85135643d577879e2e74f0a534f","source":{"kind":"arxiv","id":"2505.17804","version":1},"attestation_state":"computed","paper":{"title":"Hyperparameter Optimization via Interacting with Probabilistic Circuits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fabrizio Ventola, Jonas Seng, Kristian Kersting, Zhongjie Yu","submitted_at":"2025-05-23T12:21:19Z","abstract_excerpt":"Despite the growing interest in designing truly interactive hyperparameter optimization (HPO) methods, to date, only a few allow to include human feedback. Existing interactive Bayesian optimization (BO) methods incorporate human beliefs by weighting the acquisition function with a user-defined prior distribution. However, in light of the non-trivial inner optimization of the acquisition function prevalent in BO, such weighting schemes do not always accurately reflect given user beliefs. We introduce a novel BO approach leveraging tractable probabilistic models named probabilistic circuits (PC"},"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":"2505.17804","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-23T12:21:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"41ad5470c267625325d13fc390a6b47d60a84a95dee08f79ec998fb86ec09be5","abstract_canon_sha256":"057e556f19e9572b0076284d02adcbcaf87b8b06d5b62e43ef1adc0a25b34809"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:32.024684Z","signature_b64":"O77+SINEEETP6wOwr84Fgec/QC0mScGDn6dR/I340OzaR6M8cTQpYNKCRK7+Zo4Qv3Dsb/Lebhw8CRITdYNRBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"36ee09c8f0b82b50e6f63958eebd7de5d005e85135643d577879e2e74f0a534f","last_reissued_at":"2026-07-05T11:08:32.024269Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:32.024269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hyperparameter Optimization via Interacting with Probabilistic Circuits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Fabrizio Ventola, Jonas Seng, Kristian Kersting, Zhongjie Yu","submitted_at":"2025-05-23T12:21:19Z","abstract_excerpt":"Despite the growing interest in designing truly interactive hyperparameter optimization (HPO) methods, to date, only a few allow to include human feedback. Existing interactive Bayesian optimization (BO) methods incorporate human beliefs by weighting the acquisition function with a user-defined prior distribution. However, in light of the non-trivial inner optimization of the acquisition function prevalent in BO, such weighting schemes do not always accurately reflect given user beliefs. We introduce a novel BO approach leveraging tractable probabilistic models named probabilistic circuits (PC"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17804","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/2505.17804/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":"2505.17804","created_at":"2026-07-05T11:08:32.024334+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17804v1","created_at":"2026-07-05T11:08:32.024334+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17804","created_at":"2026-07-05T11:08:32.024334+00:00"},{"alias_kind":"pith_short_12","alias_value":"G3XATSHQXAVV","created_at":"2026-07-05T11:08:32.024334+00:00"},{"alias_kind":"pith_short_16","alias_value":"G3XATSHQXAVVBZXW","created_at":"2026-07-05T11:08:32.024334+00:00"},{"alias_kind":"pith_short_8","alias_value":"G3XATSHQ","created_at":"2026-07-05T11:08:32.024334+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/G3XATSHQXAVVBZXWHFMO5PL54X","json":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X.json","graph_json":"https://pith.science/api/pith-number/G3XATSHQXAVVBZXWHFMO5PL54X/graph.json","events_json":"https://pith.science/api/pith-number/G3XATSHQXAVVBZXWHFMO5PL54X/events.json","paper":"https://pith.science/paper/G3XATSHQ"},"agent_actions":{"view_html":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X","download_json":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X.json","view_paper":"https://pith.science/paper/G3XATSHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17804&json=true","fetch_graph":"https://pith.science/api/pith-number/G3XATSHQXAVVBZXWHFMO5PL54X/graph.json","fetch_events":"https://pith.science/api/pith-number/G3XATSHQXAVVBZXWHFMO5PL54X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X/action/storage_attestation","attest_author":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X/action/author_attestation","sign_citation":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X/action/citation_signature","submit_replication":"https://pith.science/pith/G3XATSHQXAVVBZXWHFMO5PL54X/action/replication_record"}},"created_at":"2026-07-05T11:08:32.024334+00:00","updated_at":"2026-07-05T11:08:32.024334+00:00"}