{"total":22,"rows":[{"id":"2508.07473","title":"Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications","created":"2025-08-10","cited_by_count":17,"area":null},{"id":"2411.07061","title":"General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization","created":"2024-11-11","cited_by_count":15,"area":null},{"id":"2602.08232","title":"Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization","created":"2026-02-09","cited_by_count":14,"area":null},{"id":"2507.09846","title":"Through the River: Understanding the Benefit of Schedule-Free Methods for Language Model Training","created":"2025-07-14","cited_by_count":9,"area":null},{"id":"2505.11840","title":"On the $O(\\frac{\\sqrt{d}}{K^{1/4}})$ Convergence Rate of AdamW Measured by $\\ell_1$ Norm","created":"2025-05-17","cited_by_count":8,"area":null},{"id":"2604.15416","title":"StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models","created":"2026-04-16","cited_by_count":5,"area":null},{"id":"2608.26288","title":"Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization","created":"2026-08-26","cited_by_count":5,"area":null},{"id":"2602.08372","title":"Dynamic Regret via Discounted-to-Dynamic Reduction with Applications to Curved Losses and Adam Optimizer","created":"2026-02-09","cited_by_count":4,"area":null},{"id":"2510.03167","title":"Improving Online-to-Nonconvex Conversion for Smooth Optimization via Double Optimism","created":"2025-10-03","cited_by_count":3,"area":null},{"id":"2412.19444","title":"Towards Simple and Provable Parameter-Free Adaptive Gradient Methods","created":"2024-12-27","cited_by_count":2,"area":null},{"id":"2501.19099","title":"Elucidating Subspace Perturbation in Zeroth-Order Optimization: Theory and Practice at Scale","created":"2025-01-31","cited_by_count":2,"area":null},{"id":"2510.03478","title":"How to Set $\\beta_1, \\beta_2$ in Adam: An Online Learning Perspective","created":"2025-10-03","cited_by_count":2,"area":null},{"id":"2510.26303","title":"Implicit Bias of Per-sample Adam on Separable Data: Departure from the Full-batch Regime","created":"2025-10-30","cited_by_count":2,"area":null},{"id":"2607.27383","title":"The Convergence Behavior of Adam under Heavy-Tailed Noise","created":"2026-07-29","cited_by_count":2,"area":null},{"id":"2505.22085","title":"PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning","created":"2025-05-28","cited_by_count":1,"area":null},{"id":"2606.21433","title":"Central limit theorem for the averaged Adam optimizer","created":"2026-06-19","cited_by_count":1,"area":null},{"id":"2606.23676","title":"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?","created":"2026-06-22","cited_by_count":1,"area":null},{"id":"2605.15522","title":"Stochastic Non-Smooth Convex Optimization with Unbounded Gradients","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2606.22326","title":"Adam Converges in Nonsmooth Nonconvex Optimization","created":"2026-06-21","cited_by_count":0,"area":null},{"id":"2607.17607","title":"Optimizing the Preconditioner: A Black-box Online-to-Nonconvex Conversion with Static Regret Minimization Oracles","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2609.23837","title":"The Exponential Price of Determinism in Nonsmooth Nonconvex Optimization","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2610.13787","title":"Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}