{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AS4NRZFZK3V3BS5SZW7D5772PV","short_pith_number":"pith:AS4NRZFZ","schema_version":"1.0","canonical_sha256":"04b8d8e4b956ebb0cbb2cdbe3efffa7d4069f663e3cc2e6fd13977c3717abd54","source":{"kind":"arxiv","id":"1902.10710","version":2},"attestation_state":"computed","paper":{"title":"High probability generalization bounds for uniformly stable algorithms with nearly optimal rate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jan Vondrak, Vitaly Feldman","submitted_at":"2019-02-27T18:50:28Z","abstract_excerpt":"Algorithmic stability is a classical approach to understanding and analysis of the generalization error of learning algorithms. A notable weakness of most stability-based generalization bounds is that they hold only in expectation. Generalization with high probability has been established in a landmark paper of Bousquet and Elisseeff (2002) albeit at the expense of an additional $\\sqrt{n}$ factor in the bound. Specifically, their bound on the estimation error of any $\\gamma$-uniformly stable learning algorithm on $n$ samples and range in $[0,1]$ is $O(\\gamma \\sqrt{n \\log(1/\\delta)} + \\sqrt{\\lo"},"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":"1902.10710","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-27T18:50:28Z","cross_cats_sorted":["cs.DS","stat.ML"],"title_canon_sha256":"05286b5457536767bc477d21f0d2e6ddcab062e3d45344b4e7b505256a85dcc7","abstract_canon_sha256":"7c00968113d4860e64e33be4c07cb8376b68b6485c345fe9ce9da6d286d3a199"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:42.204212Z","signature_b64":"gA/RzUa9kUeeQccBHfHKJJOxJ3G1kLkCXgETKyfp4gLgMHDGobNDYgO5fgKwbb52/SnAO6LsrepqTPAozE37DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04b8d8e4b956ebb0cbb2cdbe3efffa7d4069f663e3cc2e6fd13977c3717abd54","last_reissued_at":"2026-05-17T23:42:42.203489Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:42.203489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High probability generalization bounds for uniformly stable algorithms with nearly optimal rate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jan Vondrak, Vitaly Feldman","submitted_at":"2019-02-27T18:50:28Z","abstract_excerpt":"Algorithmic stability is a classical approach to understanding and analysis of the generalization error of learning algorithms. A notable weakness of most stability-based generalization bounds is that they hold only in expectation. Generalization with high probability has been established in a landmark paper of Bousquet and Elisseeff (2002) albeit at the expense of an additional $\\sqrt{n}$ factor in the bound. Specifically, their bound on the estimation error of any $\\gamma$-uniformly stable learning algorithm on $n$ samples and range in $[0,1]$ is $O(\\gamma \\sqrt{n \\log(1/\\delta)} + \\sqrt{\\lo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.10710","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":""},"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":"1902.10710","created_at":"2026-05-17T23:42:42.203609+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.10710v2","created_at":"2026-05-17T23:42:42.203609+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.10710","created_at":"2026-05-17T23:42:42.203609+00:00"},{"alias_kind":"pith_short_12","alias_value":"AS4NRZFZK3V3","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"AS4NRZFZK3V3BS5S","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"AS4NRZFZ","created_at":"2026-05-18T12:33:12.712433+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/AS4NRZFZK3V3BS5SZW7D5772PV","json":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV.json","graph_json":"https://pith.science/api/pith-number/AS4NRZFZK3V3BS5SZW7D5772PV/graph.json","events_json":"https://pith.science/api/pith-number/AS4NRZFZK3V3BS5SZW7D5772PV/events.json","paper":"https://pith.science/paper/AS4NRZFZ"},"agent_actions":{"view_html":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV","download_json":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV.json","view_paper":"https://pith.science/paper/AS4NRZFZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.10710&json=true","fetch_graph":"https://pith.science/api/pith-number/AS4NRZFZK3V3BS5SZW7D5772PV/graph.json","fetch_events":"https://pith.science/api/pith-number/AS4NRZFZK3V3BS5SZW7D5772PV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV/action/storage_attestation","attest_author":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV/action/author_attestation","sign_citation":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV/action/citation_signature","submit_replication":"https://pith.science/pith/AS4NRZFZK3V3BS5SZW7D5772PV/action/replication_record"}},"created_at":"2026-05-17T23:42:42.203609+00:00","updated_at":"2026-05-17T23:42:42.203609+00:00"}