{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JHK4HZLHY7S7AWOMCYWMWOIIYW","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":"df0c7c45a1be459f75d2d8c3f7bbe0bf645ea753b53899c7b7df1df1e49c1e39","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-27T04:22:02Z","title_canon_sha256":"fcfd4fbb63fa23155b08a7365454e4a66e82c8a228e0f3a48ea840e1184d8b0c"},"schema_version":"1.0","source":{"id":"2412.19444","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.19444","created_at":"2026-06-02T02:04:04Z"},{"alias_kind":"arxiv_version","alias_value":"2412.19444v2","created_at":"2026-06-02T02:04:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19444","created_at":"2026-06-02T02:04:04Z"},{"alias_kind":"pith_short_12","alias_value":"JHK4HZLHY7S7","created_at":"2026-06-02T02:04:04Z"},{"alias_kind":"pith_short_16","alias_value":"JHK4HZLHY7S7AWOM","created_at":"2026-06-02T02:04:04Z"},{"alias_kind":"pith_short_8","alias_value":"JHK4HZLH","created_at":"2026-06-02T02:04:04Z"}],"graph_snapshots":[{"event_id":"sha256:792e5ee9774379cb94bb8b8829b6287de72ec782ac3f575080bf7b35f8ec1d36","target":"graph","created_at":"2026-06-02T02:04:04Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.19444/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate during the optimization process. However, ad-hoc tuning of learning rates poses a challenge and leads to inefficiencies in practice. To address this issue, recent research has focused on developing ``parameter-free'' algorithms that operate effectively without the need for learning rate tuning. Despite these efforts, existing parameter-free variants of AdaGrad and Adam tend to be overly complex and/or lack formal convergence guarantees. In this pap","authors_text":"Huizhuo Yuan, Quanquan Gu, Xun Zhou, Yifeng Liu, Yuan Cao, Yuanzhe Tao","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-27T04:22:02Z","title":"Towards Simple and Provable Parameter-Free Adaptive Gradient Methods"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19444","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":"sha256:6d8b5b5859cefbe6e03552bc35dfae70629721b88b4d22c49de8effa3e402f94","target":"record","created_at":"2026-06-02T02:04:04Z","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":"df0c7c45a1be459f75d2d8c3f7bbe0bf645ea753b53899c7b7df1df1e49c1e39","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-27T04:22:02Z","title_canon_sha256":"fcfd4fbb63fa23155b08a7365454e4a66e82c8a228e0f3a48ea840e1184d8b0c"},"schema_version":"1.0","source":{"id":"2412.19444","kind":"arxiv","version":2}},"canonical_sha256":"49d5c3e567c7e5f059cc162ccb3908c5a8c6a127083e23ae4ec9e74cf3b63b29","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"49d5c3e567c7e5f059cc162ccb3908c5a8c6a127083e23ae4ec9e74cf3b63b29","first_computed_at":"2026-06-02T02:04:04.796180Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T02:04:04.796180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lJBp0x2yGKNyzjyp0utN9/CsrOE5dHpCedTiq23Zrj0TThnRmo5Mq+ZuVrowmK1ADO8zZQ4nk2B+lxZdUUQ1Dg==","signature_status":"signed_v1","signed_at":"2026-06-02T02:04:04.796635Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.19444","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d8b5b5859cefbe6e03552bc35dfae70629721b88b4d22c49de8effa3e402f94","sha256:792e5ee9774379cb94bb8b8829b6287de72ec782ac3f575080bf7b35f8ec1d36"],"state_sha256":"b5feefc0b2807f484495b78cd5b0795c0812918575e79eb3dcd2f5e22fc78ebc"}