{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:P6GVZFUQCAHRDIHFPXZKNY7OYN","short_pith_number":"pith:P6GVZFUQ","schema_version":"1.0","canonical_sha256":"7f8d5c9690100f11a0e57df2a6e3eec35f2d160fecabf29b8ef235ca05639863","source":{"kind":"arxiv","id":"1804.05433","version":1},"attestation_state":"computed","paper":{"title":"Adaptivity for Regularized Kernel Methods by Lepskii's Principle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Nicole M\\\"ucke","submitted_at":"2018-04-15T21:27:04Z","abstract_excerpt":"We address the problem of {\\it adaptivity} in the framework of reproducing kernel Hilbert space (RKHS) regression. More precisely, we analyze estimators arising from a linear regularization scheme $g_\\lam$. In practical applications, an important task is to choose the regularization parameter $\\lam$ appropriately, i.e. based only on the given data and independently on unknown structural assumptions on the regression function. An attractive approach avoiding data-splitting is the {\\it Lepskii Principle} (LP), also known as the {\\it Balancing Principle} is this setting. We show that a modified p"},"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":"1804.05433","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-15T21:27:04Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ec9709dc572bf101bb3030d2e941d61b24c40e89141d59a58e61f7ea8e1684db","abstract_canon_sha256":"1e6f4066f8a8eb1a9efd865e4c453c763528e54838e2bc566960d36709266abc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:18:28.151725Z","signature_b64":"N79em8VM//sQZkCZS+GgywZ6THv+/HBsdp9HKmt91m7OOBUZSeL8hv3Z1cvnAJT1DBeThK9J1bj5Ja7vsPzsBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f8d5c9690100f11a0e57df2a6e3eec35f2d160fecabf29b8ef235ca05639863","last_reissued_at":"2026-05-18T00:18:28.151161Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:18:28.151161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptivity for Regularized Kernel Methods by Lepskii's Principle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Nicole M\\\"ucke","submitted_at":"2018-04-15T21:27:04Z","abstract_excerpt":"We address the problem of {\\it adaptivity} in the framework of reproducing kernel Hilbert space (RKHS) regression. More precisely, we analyze estimators arising from a linear regularization scheme $g_\\lam$. In practical applications, an important task is to choose the regularization parameter $\\lam$ appropriately, i.e. based only on the given data and independently on unknown structural assumptions on the regression function. An attractive approach avoiding data-splitting is the {\\it Lepskii Principle} (LP), also known as the {\\it Balancing Principle} is this setting. We show that a modified p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.05433","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":""},"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":"1804.05433","created_at":"2026-05-18T00:18:28.151263+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.05433v1","created_at":"2026-05-18T00:18:28.151263+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.05433","created_at":"2026-05-18T00:18:28.151263+00:00"},{"alias_kind":"pith_short_12","alias_value":"P6GVZFUQCAHR","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"P6GVZFUQCAHRDIHF","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"P6GVZFUQ","created_at":"2026-05-18T12:32:43.782077+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/P6GVZFUQCAHRDIHFPXZKNY7OYN","json":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN.json","graph_json":"https://pith.science/api/pith-number/P6GVZFUQCAHRDIHFPXZKNY7OYN/graph.json","events_json":"https://pith.science/api/pith-number/P6GVZFUQCAHRDIHFPXZKNY7OYN/events.json","paper":"https://pith.science/paper/P6GVZFUQ"},"agent_actions":{"view_html":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN","download_json":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN.json","view_paper":"https://pith.science/paper/P6GVZFUQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.05433&json=true","fetch_graph":"https://pith.science/api/pith-number/P6GVZFUQCAHRDIHFPXZKNY7OYN/graph.json","fetch_events":"https://pith.science/api/pith-number/P6GVZFUQCAHRDIHFPXZKNY7OYN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN/action/storage_attestation","attest_author":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN/action/author_attestation","sign_citation":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN/action/citation_signature","submit_replication":"https://pith.science/pith/P6GVZFUQCAHRDIHFPXZKNY7OYN/action/replication_record"}},"created_at":"2026-05-18T00:18:28.151263+00:00","updated_at":"2026-05-18T00:18:28.151263+00:00"}