{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RKA76GQFS76KS5VHETIPKCJ6U3","short_pith_number":"pith:RKA76GQF","schema_version":"1.0","canonical_sha256":"8a81ff1a0597fca976a724d0f5093ea6e18013dcf1df3a9e50f70b0155f40a6c","source":{"kind":"arxiv","id":"2509.17405","version":2},"attestation_state":"computed","paper":{"title":"Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David Hyde, Manish Acharya","submitted_at":"2025-09-22T07:02:19Z","abstract_excerpt":"The sliced Wasserstein distance (SW) reduces optimal transport on $\\mathbb{R}^d$ to a sum of one-dimensional projections, and thanks to this efficiency, it is widely used in geometry, generative modeling, and registration tasks. Recent work shows that quasi-Monte Carlo constructions for computing SW (QSW) yield direction sets with excellent approximation error. This paper presents an alternate, novel approach: learning directions with Bayesian optimization (BO), particularly in settings where SW appears inside an optimization loop (e.g., gradient flows). We introduce a family of drop-in select"},"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":"2509.17405","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-22T07:02:19Z","cross_cats_sorted":[],"title_canon_sha256":"5ecb0b16b35aa2d5d8049137edd75867cc1395e68303a27405b52f9e969f27d7","abstract_canon_sha256":"e10e260ba53714899ac32becde97ef446459ad7efd4df9614e7ba2e280b7219e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:19:54.792824Z","signature_b64":"aTT0Pa0b0kRnBgdOMk9MOr1svAHfWl1jSp5eeyZfLbXwwMXyReeD3pTAt/bayHvtk2CmYKsOt2kHVmtsUC+oCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a81ff1a0597fca976a724d0f5093ea6e18013dcf1df3a9e50f70b0155f40a6c","last_reissued_at":"2026-07-14T01:19:54.791883Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:19:54.791883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David Hyde, Manish Acharya","submitted_at":"2025-09-22T07:02:19Z","abstract_excerpt":"The sliced Wasserstein distance (SW) reduces optimal transport on $\\mathbb{R}^d$ to a sum of one-dimensional projections, and thanks to this efficiency, it is widely used in geometry, generative modeling, and registration tasks. Recent work shows that quasi-Monte Carlo constructions for computing SW (QSW) yield direction sets with excellent approximation error. This paper presents an alternate, novel approach: learning directions with Bayesian optimization (BO), particularly in settings where SW appears inside an optimization loop (e.g., gradient flows). We introduce a family of drop-in select"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.17405","kind":"arxiv","version":2},"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/2509.17405/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":"2509.17405","created_at":"2026-07-14T01:19:54.792336+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.17405v2","created_at":"2026-07-14T01:19:54.792336+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.17405","created_at":"2026-07-14T01:19:54.792336+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKA76GQFS76K","created_at":"2026-07-14T01:19:54.792336+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKA76GQFS76KS5VH","created_at":"2026-07-14T01:19:54.792336+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKA76GQF","created_at":"2026-07-14T01:19:54.792336+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/RKA76GQFS76KS5VHETIPKCJ6U3","json":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3.json","graph_json":"https://pith.science/api/pith-number/RKA76GQFS76KS5VHETIPKCJ6U3/graph.json","events_json":"https://pith.science/api/pith-number/RKA76GQFS76KS5VHETIPKCJ6U3/events.json","paper":"https://pith.science/paper/RKA76GQF"},"agent_actions":{"view_html":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3","download_json":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3.json","view_paper":"https://pith.science/paper/RKA76GQF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.17405&json=true","fetch_graph":"https://pith.science/api/pith-number/RKA76GQFS76KS5VHETIPKCJ6U3/graph.json","fetch_events":"https://pith.science/api/pith-number/RKA76GQFS76KS5VHETIPKCJ6U3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3/action/storage_attestation","attest_author":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3/action/author_attestation","sign_citation":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3/action/citation_signature","submit_replication":"https://pith.science/pith/RKA76GQFS76KS5VHETIPKCJ6U3/action/replication_record"}},"created_at":"2026-07-14T01:19:54.792336+00:00","updated_at":"2026-07-14T01:19:54.792336+00:00"}