{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:K4YMEZ5EARS4TI7I2IGJST6YNE","short_pith_number":"pith:K4YMEZ5E","schema_version":"1.0","canonical_sha256":"5730c267a40465c9a3e8d20c994fd86905ce6b6d1fb3c71f83229ca6e5241f0e","source":{"kind":"arxiv","id":"1703.02622","version":1},"attestation_state":"computed","paper":{"title":"Online Convex Optimization with Unconstrained Domains and Losses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ashok Cutkosky, Kwabena Boahen","submitted_at":"2017-03-07T22:14:53Z","abstract_excerpt":"We propose an online convex optimization algorithm (RescaledExp) that achieves optimal regret in the unconstrained setting without prior knowledge of any bounds on the loss functions. We prove a lower bound showing an exponential separation between the regret of existing algorithms that require a known bound on the loss functions and any algorithm that does not require such knowledge. RescaledExp matches this lower bound asymptotically in the number of iterations. RescaledExp is naturally hyperparameter-free and we demonstrate empirically that it matches prior optimization algorithms that requ"},"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":"1703.02622","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-07T22:14:53Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2a76cca749d2b596f5f9c8472290cf51dedd3e0dd27e16d641a800e5fd9ed4d4","abstract_canon_sha256":"d35301a0d8dec6e46ed64d24734207b1329e25c47c257e921c177a1eeb912678"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:06.017495Z","signature_b64":"/+CSRJ+CLlUKZvp3LGvc+PYnEW2LfP7pRJ6wV3R9xopQnFcPfP8Sn6XDXOwEoxFRA4Wc06Me7t6cxrvydy2AAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5730c267a40465c9a3e8d20c994fd86905ce6b6d1fb3c71f83229ca6e5241f0e","last_reissued_at":"2026-05-18T00:49:06.017072Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:06.017072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Convex Optimization with Unconstrained Domains and Losses","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ashok Cutkosky, Kwabena Boahen","submitted_at":"2017-03-07T22:14:53Z","abstract_excerpt":"We propose an online convex optimization algorithm (RescaledExp) that achieves optimal regret in the unconstrained setting without prior knowledge of any bounds on the loss functions. We prove a lower bound showing an exponential separation between the regret of existing algorithms that require a known bound on the loss functions and any algorithm that does not require such knowledge. RescaledExp matches this lower bound asymptotically in the number of iterations. RescaledExp is naturally hyperparameter-free and we demonstrate empirically that it matches prior optimization algorithms that requ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02622","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":"1703.02622","created_at":"2026-05-18T00:49:06.017139+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.02622v1","created_at":"2026-05-18T00:49:06.017139+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02622","created_at":"2026-05-18T00:49:06.017139+00:00"},{"alias_kind":"pith_short_12","alias_value":"K4YMEZ5EARS4","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"K4YMEZ5EARS4TI7I","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"K4YMEZ5E","created_at":"2026-05-18T12:31:24.725408+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/K4YMEZ5EARS4TI7I2IGJST6YNE","json":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE.json","graph_json":"https://pith.science/api/pith-number/K4YMEZ5EARS4TI7I2IGJST6YNE/graph.json","events_json":"https://pith.science/api/pith-number/K4YMEZ5EARS4TI7I2IGJST6YNE/events.json","paper":"https://pith.science/paper/K4YMEZ5E"},"agent_actions":{"view_html":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE","download_json":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE.json","view_paper":"https://pith.science/paper/K4YMEZ5E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.02622&json=true","fetch_graph":"https://pith.science/api/pith-number/K4YMEZ5EARS4TI7I2IGJST6YNE/graph.json","fetch_events":"https://pith.science/api/pith-number/K4YMEZ5EARS4TI7I2IGJST6YNE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/action/storage_attestation","attest_author":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/action/author_attestation","sign_citation":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/action/citation_signature","submit_replication":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/action/replication_record"}},"created_at":"2026-05-18T00:49:06.017139+00:00","updated_at":"2026-05-18T00:49:06.017139+00:00"}