{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:T5JJ4MMGFX6XNESFY6DVJSELBR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"eb41ff729d2a0b3e4772e8ac395e51e1f6da09990666dcbedd36db546e58d48a","cross_cats_sorted":["cs.HC","cs.LG","math.PR","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-06-01T05:36:22Z","title_canon_sha256":"bec96503b1d2e6a383ba71184bce6d210a4c53a452490a6aa3c1457ba3a1c905"},"schema_version":"1.0","source":{"id":"2306.00357","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.00357","created_at":"2026-07-05T06:16:25Z"},{"alias_kind":"arxiv_version","alias_value":"2306.00357v1","created_at":"2026-07-05T06:16:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.00357","created_at":"2026-07-05T06:16:25Z"},{"alias_kind":"pith_short_12","alias_value":"T5JJ4MMGFX6X","created_at":"2026-07-05T06:16:25Z"},{"alias_kind":"pith_short_16","alias_value":"T5JJ4MMGFX6XNESF","created_at":"2026-07-05T06:16:25Z"},{"alias_kind":"pith_short_8","alias_value":"T5JJ4MMG","created_at":"2026-07-05T06:16:25Z"}],"graph_snapshots":[{"event_id":"sha256:55898f03ed174d4995dba19b9415099f402d308b5083e845bceeebada8997375","target":"graph","created_at":"2026-07-05T06:16:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.00357/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce an efficient and robust auto-tuning framework for hyperparameter selection in dimension reduction (DR) algorithms, focusing on large-scale datasets and arbitrary performance metrics. By leveraging Bayesian optimization (BO) with a surrogate model, our approach enables efficient hyperparameter selection with multi-objective trade-offs and allows us to perform data-driven sensitivity analysis. By incorporating normalization and subsampling, the proposed framework demonstrates versatility and efficiency, as shown in applications to visualization techniques such as t-SNE and UMAP. We ","authors_text":"Anna Ma, Hengrui Luo, Yin-Ting Liao","cross_cats":["cs.HC","cs.LG","math.PR","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-06-01T05:36:22Z","title":"Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.00357","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:25ad65af942ee6fc1f033c40790cd56d1fa3ba4f93c99e1b97e1856c025266d1","target":"record","created_at":"2026-07-05T06:16:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"eb41ff729d2a0b3e4772e8ac395e51e1f6da09990666dcbedd36db546e58d48a","cross_cats_sorted":["cs.HC","cs.LG","math.PR","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-06-01T05:36:22Z","title_canon_sha256":"bec96503b1d2e6a383ba71184bce6d210a4c53a452490a6aa3c1457ba3a1c905"},"schema_version":"1.0","source":{"id":"2306.00357","kind":"arxiv","version":1}},"canonical_sha256":"9f529e31862dfd769245c78754c88b0c6196e3d787e08e3da60387593e0bc018","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f529e31862dfd769245c78754c88b0c6196e3d787e08e3da60387593e0bc018","first_computed_at":"2026-07-05T06:16:25.387585Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:16:25.387585Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PDWhzQozvcFNrRJDN+CYwM9MmYgs0lOtJuVvOyqcP24VArvijP27lCdB+2i0vNmlmfjgDHSEByAXaSfBAIolDw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:16:25.388095Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.00357","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25ad65af942ee6fc1f033c40790cd56d1fa3ba4f93c99e1b97e1856c025266d1","sha256:55898f03ed174d4995dba19b9415099f402d308b5083e845bceeebada8997375"],"state_sha256":"99d328cec5fb862c076c157e1585700dc8a5156684ad91da1dfe6d0f2c9a8335"}