{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JHK4HZLHY7S7AWOMCYWMWOIIYW","short_pith_number":"pith:JHK4HZLH","schema_version":"1.0","canonical_sha256":"49d5c3e567c7e5f059cc162ccb3908c5a8c6a127083e23ae4ec9e74cf3b63b29","source":{"kind":"arxiv","id":"2412.19444","version":2},"attestation_state":"computed","paper":{"title":"Towards Simple and Provable Parameter-Free Adaptive Gradient Methods","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Huizhuo Yuan, Quanquan Gu, Xun Zhou, Yifeng Liu, Yuan Cao, Yuanzhe Tao","submitted_at":"2024-12-27T04:22:02Z","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"},"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":"2412.19444","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-27T04:22:02Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"fcfd4fbb63fa23155b08a7365454e4a66e82c8a228e0f3a48ea840e1184d8b0c","abstract_canon_sha256":"df0c7c45a1be459f75d2d8c3f7bbe0bf645ea753b53899c7b7df1df1e49c1e39"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:04.796635Z","signature_b64":"lJBp0x2yGKNyzjyp0utN9/CsrOE5dHpCedTiq23Zrj0TThnRmo5Mq+ZuVrowmK1ADO8zZQ4nk2B+lxZdUUQ1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49d5c3e567c7e5f059cc162ccb3908c5a8c6a127083e23ae4ec9e74cf3b63b29","last_reissued_at":"2026-06-02T02:04:04.796180Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:04.796180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Simple and Provable Parameter-Free Adaptive Gradient Methods","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Huizhuo Yuan, Quanquan Gu, Xun Zhou, Yifeng Liu, Yuan Cao, Yuanzhe Tao","submitted_at":"2024-12-27T04:22:02Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19444","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.19444/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2412.19444","created_at":"2026-06-02T02:04:04.796242+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19444v2","created_at":"2026-06-02T02:04:04.796242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19444","created_at":"2026-06-02T02:04:04.796242+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHK4HZLHY7S7","created_at":"2026-06-02T02:04:04.796242+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHK4HZLHY7S7AWOM","created_at":"2026-06-02T02:04:04.796242+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHK4HZLH","created_at":"2026-06-02T02:04:04.796242+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/JHK4HZLHY7S7AWOMCYWMWOIIYW","json":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW.json","graph_json":"https://pith.science/api/pith-number/JHK4HZLHY7S7AWOMCYWMWOIIYW/graph.json","events_json":"https://pith.science/api/pith-number/JHK4HZLHY7S7AWOMCYWMWOIIYW/events.json","paper":"https://pith.science/paper/JHK4HZLH"},"agent_actions":{"view_html":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW","download_json":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW.json","view_paper":"https://pith.science/paper/JHK4HZLH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19444&json=true","fetch_graph":"https://pith.science/api/pith-number/JHK4HZLHY7S7AWOMCYWMWOIIYW/graph.json","fetch_events":"https://pith.science/api/pith-number/JHK4HZLHY7S7AWOMCYWMWOIIYW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW/action/storage_attestation","attest_author":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW/action/author_attestation","sign_citation":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW/action/citation_signature","submit_replication":"https://pith.science/pith/JHK4HZLHY7S7AWOMCYWMWOIIYW/action/replication_record"}},"created_at":"2026-06-02T02:04:04.796242+00:00","updated_at":"2026-06-02T02:04:04.796242+00:00"}