{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:Y7MFRDNMMB5TFMYAAGQWSOFB6J","short_pith_number":"pith:Y7MFRDNM","canonical_record":{"source":{"id":"1901.11500","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-31T18:02:47Z","cross_cats_sorted":[],"title_canon_sha256":"3d6a222513a3bc61240a4b3fac0d2b1363ec9009c220fe7cc982d6a2c365681f","abstract_canon_sha256":"d2dd32bbaa4d33c41ac4637b87ba6735e3c5a7f390848d6be6b0f6f4a0a7b561"},"schema_version":"1.0"},"canonical_sha256":"c7d8588dac607b32b30001a16938a1f2575fd18a6d432e7df040c8b40648b1ae","source":{"kind":"arxiv","id":"1901.11500","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.11500","created_at":"2026-05-17T23:54:58Z"},{"alias_kind":"arxiv_version","alias_value":"1901.11500v2","created_at":"2026-05-17T23:54:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.11500","created_at":"2026-05-17T23:54:58Z"},{"alias_kind":"pith_short_12","alias_value":"Y7MFRDNMMB5T","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Y7MFRDNMMB5TFMYA","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Y7MFRDNM","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:Y7MFRDNMMB5TFMYAAGQWSOFB6J","target":"record","payload":{"canonical_record":{"source":{"id":"1901.11500","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-31T18:02:47Z","cross_cats_sorted":[],"title_canon_sha256":"3d6a222513a3bc61240a4b3fac0d2b1363ec9009c220fe7cc982d6a2c365681f","abstract_canon_sha256":"d2dd32bbaa4d33c41ac4637b87ba6735e3c5a7f390848d6be6b0f6f4a0a7b561"},"schema_version":"1.0"},"canonical_sha256":"c7d8588dac607b32b30001a16938a1f2575fd18a6d432e7df040c8b40648b1ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:58.414623Z","signature_b64":"7wGPcaqaxkZ3NubfXMJgBWMUGsu5BM1ba36SUW80ojwp2/p6mRuJ9XuY/VseLdoEDRqfB3Kz3agEch+4C/UiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7d8588dac607b32b30001a16938a1f2575fd18a6d432e7df040c8b40648b1ae","last_reissued_at":"2026-05-17T23:54:58.414084Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:58.414084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.11500","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:54:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9AQZYev5uJN2S1DDz3lVqMKKEsniFfvShU5cvG6w4Jq8KB8L53rq3NXSShOyogszLhRwf4lSqjwoZGS+7tH0Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:56:08.547329Z"},"content_sha256":"017debf8842622cd8fe6d17673a8fe94a053c46a284395206bc8e2947855f9c8","schema_version":"1.0","event_id":"sha256:017debf8842622cd8fe6d17673a8fe94a053c46a284395206bc8e2947855f9c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:Y7MFRDNMMB5TFMYAAGQWSOFB6J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prediction in Online Convex Optimization for Parametrizable Objective Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Robert Ravier, Vahid Tarokh","submitted_at":"2019-01-31T18:02:47Z","abstract_excerpt":"Many techniques for online optimization problems involve making decisions based solely on presently available information: fewer works take advantage of potential predictions. In this paper, we discuss the problem of online convex optimization for parametrizable objectives, i.e. optimization problems that depend solely on the value of a parameter at a given time. We introduce a new regularity for dynamic regret based on the accuracy of predicted values of the parameters and show that, under mild assumptions, accurate prediction can yield tighter bounds on dynamic regret. Inspired by recent adv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.11500","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:54:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"orWHP/BnlJIOd1NWq6ZEvns9w1M/RHLOQzYLXZIznAKRdXJRQPBkScTyYf6Gx9bnk5NGPBH1CX4KEUDVFUOCAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T07:56:08.547750Z"},"content_sha256":"c83506bc70756214d657c9a5ba0719ea3c04096dc468f8914587fe633523b337","schema_version":"1.0","event_id":"sha256:c83506bc70756214d657c9a5ba0719ea3c04096dc468f8914587fe633523b337"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y7MFRDNMMB5TFMYAAGQWSOFB6J/bundle.json","state_url":"https://pith.science/pith/Y7MFRDNMMB5TFMYAAGQWSOFB6J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y7MFRDNMMB5TFMYAAGQWSOFB6J/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":"2026-05-26T07:56:08Z","links":{"resolver":"https://pith.science/pith/Y7MFRDNMMB5TFMYAAGQWSOFB6J","bundle":"https://pith.science/pith/Y7MFRDNMMB5TFMYAAGQWSOFB6J/bundle.json","state":"https://pith.science/pith/Y7MFRDNMMB5TFMYAAGQWSOFB6J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y7MFRDNMMB5TFMYAAGQWSOFB6J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:Y7MFRDNMMB5TFMYAAGQWSOFB6J","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":"d2dd32bbaa4d33c41ac4637b87ba6735e3c5a7f390848d6be6b0f6f4a0a7b561","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-31T18:02:47Z","title_canon_sha256":"3d6a222513a3bc61240a4b3fac0d2b1363ec9009c220fe7cc982d6a2c365681f"},"schema_version":"1.0","source":{"id":"1901.11500","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.11500","created_at":"2026-05-17T23:54:58Z"},{"alias_kind":"arxiv_version","alias_value":"1901.11500v2","created_at":"2026-05-17T23:54:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.11500","created_at":"2026-05-17T23:54:58Z"},{"alias_kind":"pith_short_12","alias_value":"Y7MFRDNMMB5T","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Y7MFRDNMMB5TFMYA","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Y7MFRDNM","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:c83506bc70756214d657c9a5ba0719ea3c04096dc468f8914587fe633523b337","target":"graph","created_at":"2026-05-17T23:54:58Z","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":"Many techniques for online optimization problems involve making decisions based solely on presently available information: fewer works take advantage of potential predictions. In this paper, we discuss the problem of online convex optimization for parametrizable objectives, i.e. optimization problems that depend solely on the value of a parameter at a given time. We introduce a new regularity for dynamic regret based on the accuracy of predicted values of the parameters and show that, under mild assumptions, accurate prediction can yield tighter bounds on dynamic regret. Inspired by recent adv","authors_text":"Robert Ravier, Vahid Tarokh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-31T18:02:47Z","title":"Prediction in Online Convex Optimization for Parametrizable Objective Functions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.11500","kind":"arxiv","version":2},"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":"sha256:017debf8842622cd8fe6d17673a8fe94a053c46a284395206bc8e2947855f9c8","target":"record","created_at":"2026-05-17T23:54:58Z","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":"d2dd32bbaa4d33c41ac4637b87ba6735e3c5a7f390848d6be6b0f6f4a0a7b561","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-31T18:02:47Z","title_canon_sha256":"3d6a222513a3bc61240a4b3fac0d2b1363ec9009c220fe7cc982d6a2c365681f"},"schema_version":"1.0","source":{"id":"1901.11500","kind":"arxiv","version":2}},"canonical_sha256":"c7d8588dac607b32b30001a16938a1f2575fd18a6d432e7df040c8b40648b1ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c7d8588dac607b32b30001a16938a1f2575fd18a6d432e7df040c8b40648b1ae","first_computed_at":"2026-05-17T23:54:58.414084Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:54:58.414084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7wGPcaqaxkZ3NubfXMJgBWMUGsu5BM1ba36SUW80ojwp2/p6mRuJ9XuY/VseLdoEDRqfB3Kz3agEch+4C/UiCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:54:58.414623Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.11500","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:017debf8842622cd8fe6d17673a8fe94a053c46a284395206bc8e2947855f9c8","sha256:c83506bc70756214d657c9a5ba0719ea3c04096dc468f8914587fe633523b337"],"state_sha256":"565d58ae8058c0b4a87bff411abeb096c71cc3a864ea8e95dbbd480946b62728"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bBNfhS0LPerQRaw5EWu+wrBjgVqb2lcXYbUBZHgPbzs+Y98CAzbirwXzZPQbYzK3oLl1qba6xTatkT5GjLbxBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T07:56:08.550651Z","bundle_sha256":"c6fa8fee454369b59c8bf01d1916edd7a9f359b45bb93c666d0da8f6aad8b753"}}