{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:E45AYXLZMJCJNQBKUU5RHFMSZM","short_pith_number":"pith:E45AYXLZ","schema_version":"1.0","canonical_sha256":"273a0c5d79624496c02aa53b139592cb2fe045c10bb40cb523f959f798320791","source":{"kind":"arxiv","id":"2605.28533","version":1},"attestation_state":"computed","paper":{"title":"Semi-Supervised Hypothesis Testing by Betting on Predictions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elad Tolochinsky, Yaniv Romano, Yaniv Tenzer","submitted_at":"2026-05-27T14:28:28Z","abstract_excerpt":"We introduce a testing-by-betting framework that leverages predictions on unlabeled data to enhance the power of sequential hypothesis testing. Given limited samples from the joint distribution of $(X,Y)$, and additional unlabeled samples from the marginal of $X$, we ask how unlabeled data can be used to hypothesize about the distribution of $Y$, and the conditional distribution of $Y\\mid X$. We introduce an e-statistic and use it to construct a sequential test. Under standard distributional assumptions -- label shift or concept shift -- we establish that the test is anytime valid. Furthermore"},"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":"2605.28533","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T14:28:28Z","cross_cats_sorted":[],"title_canon_sha256":"196b916170d91a799038f46d4c33b7094ce438a4adae08f38d4a1e18e307e43d","abstract_canon_sha256":"6e765cb3aaa848a521fcbb21cb1f6c39d248551c27e5550385a451dac36fb248"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T02:04:55.634626Z","signature_b64":"mBeHq/qWE1MFQnyB4xQUx2CzLptCv1SaBOne3Pv7uPfGK0gFlVuDi7OqVU8eJEyc2Ve5QZSpm9O/QWA9lQ95Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"273a0c5d79624496c02aa53b139592cb2fe045c10bb40cb523f959f798320791","last_reissued_at":"2026-05-28T02:04:55.634078Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T02:04:55.634078Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semi-Supervised Hypothesis Testing by Betting on Predictions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Elad Tolochinsky, Yaniv Romano, Yaniv Tenzer","submitted_at":"2026-05-27T14:28:28Z","abstract_excerpt":"We introduce a testing-by-betting framework that leverages predictions on unlabeled data to enhance the power of sequential hypothesis testing. Given limited samples from the joint distribution of $(X,Y)$, and additional unlabeled samples from the marginal of $X$, we ask how unlabeled data can be used to hypothesize about the distribution of $Y$, and the conditional distribution of $Y\\mid X$. We introduce an e-statistic and use it to construct a sequential test. Under standard distributional assumptions -- label shift or concept shift -- we establish that the test is anytime valid. Furthermore"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28533","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/2605.28533/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":"2605.28533","created_at":"2026-05-28T02:04:55.634152+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.28533v1","created_at":"2026-05-28T02:04:55.634152+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28533","created_at":"2026-05-28T02:04:55.634152+00:00"},{"alias_kind":"pith_short_12","alias_value":"E45AYXLZMJCJ","created_at":"2026-05-28T02:04:55.634152+00:00"},{"alias_kind":"pith_short_16","alias_value":"E45AYXLZMJCJNQBK","created_at":"2026-05-28T02:04:55.634152+00:00"},{"alias_kind":"pith_short_8","alias_value":"E45AYXLZ","created_at":"2026-05-28T02:04:55.634152+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/E45AYXLZMJCJNQBKUU5RHFMSZM","json":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM.json","graph_json":"https://pith.science/api/pith-number/E45AYXLZMJCJNQBKUU5RHFMSZM/graph.json","events_json":"https://pith.science/api/pith-number/E45AYXLZMJCJNQBKUU5RHFMSZM/events.json","paper":"https://pith.science/paper/E45AYXLZ"},"agent_actions":{"view_html":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM","download_json":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM.json","view_paper":"https://pith.science/paper/E45AYXLZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.28533&json=true","fetch_graph":"https://pith.science/api/pith-number/E45AYXLZMJCJNQBKUU5RHFMSZM/graph.json","fetch_events":"https://pith.science/api/pith-number/E45AYXLZMJCJNQBKUU5RHFMSZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM/action/storage_attestation","attest_author":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM/action/author_attestation","sign_citation":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM/action/citation_signature","submit_replication":"https://pith.science/pith/E45AYXLZMJCJNQBKUU5RHFMSZM/action/replication_record"}},"created_at":"2026-05-28T02:04:55.634152+00:00","updated_at":"2026-05-28T02:04:55.634152+00:00"}