{"total":23,"rows":[{"id":"2308.05621","title":"Normalized Gradients for All","created":"2023-08-10","cited_by_count":20,"area":null},{"id":"2504.09951","title":"Towards Weaker Variance Assumptions for Stochastic Optimization","created":"2025-04-14","cited_by_count":17,"area":null},{"id":"2402.13903","title":"Dealing with unbounded gradients in stochastic saddle-point optimization","created":"2024-02-21","cited_by_count":16,"area":null},{"id":"2402.10898","title":"The Price of Adaptivity in Stochastic Convex Optimization","created":"2024-02-16","cited_by_count":11,"area":null},{"id":"2408.09074","title":"Gradient-Variation Online Learning under Generalized Smoothness","created":"2024-08-17","cited_by_count":11,"area":null},{"id":"2405.19175","title":"Online Linear Regression in Dynamic Environments via Discounting","created":"2024-05-29","cited_by_count":10,"area":null},{"id":"2405.20540","title":"Fully Unconstrained Online Learning","created":"2024-05-30","cited_by_count":10,"area":null},{"id":"2406.01577","title":"An Equivalence Between Static and Dynamic Regret Minimization","created":"2024-06-03","cited_by_count":9,"area":null},{"id":"2506.10616","title":"Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability","created":"2025-06-12","cited_by_count":7,"area":null},{"id":"2405.19705","title":"Universal Online Convex Optimization with $1$ Projection per Round","created":"2024-05-30","cited_by_count":6,"area":null},{"id":"2603.20826","title":"Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness","created":"2026-03-21","cited_by_count":6,"area":null},{"id":"2507.05478","title":"Dynamic Regret Reduces to Kernelized Static Regret","created":"2025-07-07","cited_by_count":5,"area":null},{"id":"2604.16620","title":"Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance","created":"2026-04-17","cited_by_count":5,"area":null},{"id":"2310.01756","title":"Improved Algorithms for Adversarial Bandits with Unbounded Losses","created":"2023-10-03","cited_by_count":3,"area":null},{"id":"2505.23081","title":"Gradient Methods with Online Scaling Part I. Theoretical Foundations","created":"2025-05-29","cited_by_count":3,"area":null},{"id":"2510.04685","title":"Parameter-free Algorithms for the Stochastically Extended Adversarial Model","created":"2025-10-06","cited_by_count":3,"area":null},{"id":"2602.06902","title":"Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory","created":"2026-02-06","cited_by_count":2,"area":null},{"id":"2603.25916","title":"Parameter-Free Dynamic Regret for Unconstrained Linear Bandits","created":"2026-03-26","cited_by_count":2,"area":null},{"id":"2606.03831","title":"Online Learning with Gradient-Variation Interval Regret","created":"2026-06-02","cited_by_count":2,"area":null},{"id":"2604.11151","title":"Gradient-Variation Regret Bounds for Unconstrained Online Learning","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2408.06297","title":"LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2603.28201","title":"A Perturbation Approach to Unconstrained Linear Bandits","created":"2026-03-30","cited_by_count":0,"area":null},{"id":"2605.07531","title":"SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching","created":"2026-05-08","cited_by_count":0,"area":null}],"next_cursor":null}