{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:K4YMEZ5EARS4TI7I2IGJST6YNE","short_pith_number":"pith:K4YMEZ5E","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"},"canonical_sha256":"5730c267a40465c9a3e8d20c994fd86905ce6b6d1fb3c71f83229ca6e5241f0e","source":{"kind":"arxiv","id":"1703.02622","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02622","created_at":"2026-05-18T00:49:06Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02622v1","created_at":"2026-05-18T00:49:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02622","created_at":"2026-05-18T00:49:06Z"},{"alias_kind":"pith_short_12","alias_value":"K4YMEZ5EARS4","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"K4YMEZ5EARS4TI7I","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"K4YMEZ5E","created_at":"2026-05-18T12:31:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:K4YMEZ5EARS4TI7I2IGJST6YNE","target":"record","payload":{"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"},"canonical_sha256":"5730c267a40465c9a3e8d20c994fd86905ce6b6d1fb3c71f83229ca6e5241f0e","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"},"source_kind":"arxiv","source_id":"1703.02622","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:49:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HHFufjxzvrAP/+Rroe1EkYZdeL4JhEzHM6+AGkSF34iEXQFZKCRhxpHVjBnqAFJt2jG2iBVZz9RO36DTZhHrAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T01:41:32.621780Z"},"content_sha256":"e26f39ad92024b696a9bba55aaff2851cccaa2c89f2e487982b0f5600941b37b","schema_version":"1.0","event_id":"sha256:e26f39ad92024b696a9bba55aaff2851cccaa2c89f2e487982b0f5600941b37b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:K4YMEZ5EARS4TI7I2IGJST6YNE","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:49:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SzGc1wBrBOSXltO54+Q8vPtIXb7JiOnnoiVK5IIK5QE01zRKiXqMLBxWu1POxcpjavBAOQZ6O9i1vKk804M1AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T01:41:32.622120Z"},"content_sha256":"f0de085c5574abbff812b27686d225df3cef3b1f75e50970feaff0a8b9a258c3","schema_version":"1.0","event_id":"sha256:f0de085c5574abbff812b27686d225df3cef3b1f75e50970feaff0a8b9a258c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/bundle.json","state_url":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-20T01:41:32Z","links":{"resolver":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE","bundle":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/bundle.json","state":"https://pith.science/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K4YMEZ5EARS4TI7I2IGJST6YNE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:K4YMEZ5EARS4TI7I2IGJST6YNE","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":"d35301a0d8dec6e46ed64d24734207b1329e25c47c257e921c177a1eeb912678","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-07T22:14:53Z","title_canon_sha256":"2a76cca749d2b596f5f9c8472290cf51dedd3e0dd27e16d641a800e5fd9ed4d4"},"schema_version":"1.0","source":{"id":"1703.02622","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02622","created_at":"2026-05-18T00:49:06Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02622v1","created_at":"2026-05-18T00:49:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02622","created_at":"2026-05-18T00:49:06Z"},{"alias_kind":"pith_short_12","alias_value":"K4YMEZ5EARS4","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_16","alias_value":"K4YMEZ5EARS4TI7I","created_at":"2026-05-18T12:31:24Z"},{"alias_kind":"pith_short_8","alias_value":"K4YMEZ5E","created_at":"2026-05-18T12:31:24Z"}],"graph_snapshots":[{"event_id":"sha256:f0de085c5574abbff812b27686d225df3cef3b1f75e50970feaff0a8b9a258c3","target":"graph","created_at":"2026-05-18T00:49:06Z","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"},"paper":{"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","authors_text":"Ashok Cutkosky, Kwabena Boahen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-07T22:14:53Z","title":"Online Convex Optimization with Unconstrained Domains and Losses"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02622","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:e26f39ad92024b696a9bba55aaff2851cccaa2c89f2e487982b0f5600941b37b","target":"record","created_at":"2026-05-18T00:49:06Z","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":"d35301a0d8dec6e46ed64d24734207b1329e25c47c257e921c177a1eeb912678","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-03-07T22:14:53Z","title_canon_sha256":"2a76cca749d2b596f5f9c8472290cf51dedd3e0dd27e16d641a800e5fd9ed4d4"},"schema_version":"1.0","source":{"id":"1703.02622","kind":"arxiv","version":1}},"canonical_sha256":"5730c267a40465c9a3e8d20c994fd86905ce6b6d1fb3c71f83229ca6e5241f0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5730c267a40465c9a3e8d20c994fd86905ce6b6d1fb3c71f83229ca6e5241f0e","first_computed_at":"2026-05-18T00:49:06.017072Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:06.017072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/+CSRJ+CLlUKZvp3LGvc+PYnEW2LfP7pRJ6wV3R9xopQnFcPfP8Sn6XDXOwEoxFRA4Wc06Me7t6cxrvydy2AAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:06.017495Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.02622","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e26f39ad92024b696a9bba55aaff2851cccaa2c89f2e487982b0f5600941b37b","sha256:f0de085c5574abbff812b27686d225df3cef3b1f75e50970feaff0a8b9a258c3"],"state_sha256":"30675d579f962a51d4e518b465e28b6fcfb0f247d79c2517f35eb448764d4fda"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fKhzq4tg3Ig+cAZmdOlReZdLCUzY+gpyn9JXShba2ISkEmk+rY3OQ86X9Hw5g3DkKzio/CBlN1KFyLTdrHm2DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T01:41:32.624113Z","bundle_sha256":"b45ff491013a559e7f12a011f3b81a2398f831d87212c702209962a7755293ce"}}