{"total":97,"rows":[{"id":"2208.11195","title":"Robustness to Unbounded Smoothness of Generalized SignSGD","created":"2022-08-23","cited_by_count":81,"area":null},{"id":"2206.04030","title":"High-dimensional limit theorems for SGD: Effective dynamics and critical scaling","created":"2022-06-08","cited_by_count":70,"area":null},{"id":"2302.14843","title":"High Probability Convergence of Stochastic Gradient Methods","created":"2023-02-28","cited_by_count":49,"area":null},{"id":"2102.03183","title":"Last iterate convergence of SGD for Least-Squares in the Interpolation regime","created":"2021-02-05","cited_by_count":42,"area":null},{"id":"2103.12024","title":"Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$","created":"2021-03-22","cited_by_count":42,"area":null},{"id":"2304.09167","title":"Optimal PAC Bounds Without Uniform Convergence","created":"2023-04-18","cited_by_count":33,"area":null},{"id":"2302.08783","title":"SGD with AdaGrad Stepsizes: Full Adaptivity with High Probability to Unknown Parameters, Unbounded Gradients and Affine Variance","created":"2023-02-17","cited_by_count":30,"area":null},{"id":"2102.09393","title":"On the Convergence of Step Decay Step-Size for Stochastic Optimization","created":"2021-02-18","cited_by_count":21,"area":null},{"id":"2201.11411","title":"Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the $O(\\epsilon^{-7/4})$ Complexity","created":"2022-01-27","cited_by_count":17,"area":null},{"id":"2202.06915","title":"Stochastic linear optimization never overfits with quadratically-bounded losses on general data","created":"2022-02-14","cited_by_count":17,"area":null},{"id":"2108.07356","title":"Stochastic Optimization under Distributional Drift","created":"2021-08-16","cited_by_count":15,"area":null},{"id":"2310.11428","title":"Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression","created":"2023-10-17","cited_by_count":14,"area":null},{"id":"2507.11274","title":"Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime","created":"2025-07-15","cited_by_count":12,"area":null},{"id":"2206.11124","title":"A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta","created":"2022-06-22","cited_by_count":11,"area":null},{"id":"2306.11497","title":"Convergence and concentration properties of constant step-size SGD through Markov chains","created":"2023-06-20","cited_by_count":11,"area":null},{"id":"2403.07723","title":"On the Last-Iterate Convergence of Shuffling Gradient Methods","created":"2024-03-12","cited_by_count":11,"area":null},{"id":"2102.07002","title":"On the Last Iterate Convergence of Momentum Methods","created":"2021-02-13","cited_by_count":10,"area":null},{"id":"2108.11483","title":"Heavy-tailed Streaming Statistical Estimation","created":"2021-08-25","cited_by_count":10,"area":null},{"id":"2307.14642","title":"Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?","created":"2023-07-27","cited_by_count":10,"area":null},{"id":"2308.07588","title":"High-Probability Risk Bounds via Sequential Predictors","created":"2023-08-15","cited_by_count":10,"area":null},{"id":"2409.17357","title":"Revisiting inverse Hessian vector products for calculating influence functions","created":"2024-09-25","cited_by_count":10,"area":null},{"id":"2502.06719","title":"Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent","created":"2025-02-10","cited_by_count":10,"area":null},{"id":"2602.03702","title":"Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging","created":"2026-02-03","cited_by_count":10,"area":null},{"id":"2208.08567","title":"High Probability Bounds for Stochastic Subgradient Schemes with Heavy Tailed Noise","created":"2022-08-17","cited_by_count":9,"area":null},{"id":"2211.00969","title":"Large deviations rates for stochastic gradient descent with strongly convex functions","created":"2022-11-02","cited_by_count":9,"area":null},{"id":"2312.14567","title":"Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise","created":"2023-12-22","cited_by_count":9,"area":null},{"id":"2202.04296","title":"A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization","created":"2022-02-09","cited_by_count":8,"area":null},{"id":"2307.06915","title":"Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality","created":"2023-07-13","cited_by_count":8,"area":null},{"id":"2308.04077","title":"Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients","created":"2023-08-08","cited_by_count":8,"area":null},{"id":"2102.07314","title":"The Role of Momentum Parameters in the Optimal Convergence of Adaptive Polyak's Heavy-ball Methods","created":"2021-02-15","cited_by_count":7,"area":null},{"id":"2106.09815","title":"Escaping strict saddle points of the Moreau envelope in nonsmooth optimization","created":"2021-06-17","cited_by_count":7,"area":null},{"id":"2111.07952","title":"Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits","created":"2021-11-15","cited_by_count":7,"area":null},{"id":"2310.18784","title":"High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise","created":"2023-10-28","cited_by_count":7,"area":null},{"id":"2312.07142","title":"General Tail Bounds for Non-Smooth Stochastic Mirror Descent","created":"2023-12-12","cited_by_count":7,"area":null},{"id":"2410.15637","title":"Large Deviation Upper Bounds and Improved MSE Rates of Nonlinear SGD: Heavy-tailed Noise and Power of Symmetry","created":"2024-10-21","cited_by_count":7,"area":null},{"id":"2306.09222","title":"Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization","created":"2023-06-15","cited_by_count":6,"area":null},{"id":"2404.02241","title":"Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better","created":"2024-04-02","cited_by_count":6,"area":"diffusion"},{"id":"2503.07324","title":"Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics","created":"2025-03-10","cited_by_count":6,"area":null},{"id":"2601.16403","title":"Towards a Theoretical Understanding to the Generalization of RLHF","created":"2026-01-23","cited_by_count":6,"area":"alignment"},{"id":"2104.14173","title":"Fine-grained Generalization Analysis of Vector-valued Learning","created":"2021-04-29","cited_by_count":5,"area":null},{"id":"2301.11936","title":"Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation","created":"2023-01-27","cited_by_count":5,"area":null},{"id":"2307.13680","title":"High Probability Analysis for Non-Convex Stochastic Optimization with Clipping","created":"2023-07-25","cited_by_count":5,"area":null},{"id":"2311.11841","title":"High Probability Guarantees for Random Reshuffling","created":"2023-11-20","cited_by_count":5,"area":null},{"id":"2106.05397","title":"From inexact optimization to learning via gradient concentration","created":"2021-06-09","cited_by_count":4,"area":null},{"id":"2106.09028","title":"Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning","created":"2021-06-16","cited_by_count":4,"area":null},{"id":"2109.14375","title":"Dynamic Regret Analysis for Online Meta-Learning","created":"2021-09-29","cited_by_count":4,"area":null},{"id":"2306.00026","title":"Efficient Stochastic Approximation of Minimax Excess Risk Optimization","created":"2023-05-31","cited_by_count":4,"area":null},{"id":"2502.05305","title":"Online Covariance Estimation in Nonsmooth Stochastic Approximation","created":"2025-02-07","cited_by_count":4,"area":null},{"id":"2507.17316","title":"Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability","created":"2025-07-23","cited_by_count":4,"area":null},{"id":"2512.13123","title":"Stopping Rules for Stochastic Gradient Descent via Anytime-Valid Confidence Sequences","created":"2025-12-15","cited_by_count":4,"area":null},{"id":"2604.13870","title":"Gradient Descent's Last Iterate is Often (slightly) Suboptimal","created":"2026-04-15","cited_by_count":4,"area":null},{"id":"2206.03834","title":"Boosting the Confidence of Generalization for $L_2$-Stable Randomized Learning Algorithms","created":"2022-06-08","cited_by_count":3,"area":null},{"id":"2308.08070","title":"Max-affine regression via first-order methods","created":"2023-08-15","cited_by_count":3,"area":null},{"id":"2603.22563","title":"Privacy-Preserving Reinforcement Learning from Human Feedback via Decoupled Reward Modeling","created":"2026-03-23","cited_by_count":3,"area":"alignment"},{"id":"2302.09815","title":"On the Stability and Generalization of Triplet Learning","created":"2023-02-20","cited_by_count":2,"area":null},{"id":"2310.12969","title":"Demystifying the Myths and Legends of Nonconvex Convergence of SGD","created":"2023-10-19","cited_by_count":2,"area":null},{"id":"2311.07411","title":"A Large Deviations Perspective on Policy Gradient Algorithms","created":"2023-11-13","cited_by_count":2,"area":null},{"id":"2401.15890","title":"Probabilistic Guarantees of Stochastic Recursive Gradient in Non-Convex Finite Sum Problems","created":"2024-01-29","cited_by_count":2,"area":null},{"id":"2505.22781","title":"Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2510.12013","title":"Statistical Guarantees for High-Dimensional Stochastic Gradient Descent","created":"2025-10-13","cited_by_count":2,"area":null},{"id":"2604.09909","title":"Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size","created":"2026-04-10","cited_by_count":2,"area":null},{"id":"2606.08028","title":"Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization","created":"2026-06-06","cited_by_count":2,"area":null},{"id":"2106.14588","title":"The Convergence Rate of SGD's Final Iterate: Analysis on Dimension Dependence","created":"2021-06-28","cited_by_count":1,"area":null},{"id":"2210.00679","title":"High Probability Convergence for Accelerated Stochastic Mirror Descent","created":"2022-10-03","cited_by_count":1,"area":null},{"id":"2210.01864","title":"Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints","created":"2022-10-04","cited_by_count":1,"area":null},{"id":"2302.09967","title":"Stability-based Generalization Analysis for Mixtures of Pointwise and Pairwise Learning","created":"2023-02-20","cited_by_count":1,"area":null},{"id":"2403.02873","title":"A General Reduction for High-Probability Analysis with General Light-Tailed Distributions","created":"2024-03-05","cited_by_count":1,"area":null},{"id":"2408.12209","title":"Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization","created":"2024-08-22","cited_by_count":1,"area":null},{"id":"2410.06878","title":"Noise is All You Need: Private Second-Order Convergence of Noisy SGD","created":"2024-10-09","cited_by_count":1,"area":null},{"id":"2410.13954","title":"Nonlinear Stochastic Gradient Descent and Heavy-tailed Noise: A Unified Framework and High-probability Guarantees","created":"2024-10-17","cited_by_count":1,"area":null},{"id":"2411.06770","title":"Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis","created":"2024-11-11","cited_by_count":1,"area":null},{"id":"2509.13628","title":"Accelerated Gradient Methods with Biased Gradient Estimates: Risk Sensitivity, High-Probability Guarantees, and Large Deviation Bounds","created":"2025-09-17","cited_by_count":1,"area":null},{"id":"2510.06141","title":"High-Probability Convergence Guarantees of Decentralized SGD","created":"2025-10-07","cited_by_count":1,"area":null},{"id":"2511.02258","title":"Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks","created":"2025-11-04","cited_by_count":1,"area":null},{"id":"2601.20399","title":"Convergence Analysis of Randomized Subspace Normalized SGD under Heavy-Tailed Noise","created":"2026-01-28","cited_by_count":1,"area":null},{"id":"2602.05657","title":"Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2604.28020","title":"Cost-Aware Learning","created":"2026-04-30","cited_by_count":1,"area":null},{"id":"2605.04269","title":"Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2609.08537","title":"The Exact Time-Uniform Rate Frontier for Stochastic Gradient Descent on Smooth Convex Objectives","created":"2026-09-08","cited_by_count":1,"area":null},{"id":"2111.05232","title":"Learning Rates for Nonconvex Pairwise Learning","created":"2021-11-09","cited_by_count":0,"area":null},{"id":"2209.13803","title":"FedVeca: Federated Vectorized Averaging on Non-IID Data with Adaptive Bi-directional Global Objective","created":"2022-09-28","cited_by_count":0,"area":null},{"id":"2403.16557","title":"Accelerating Federated Learning by Selecting Beneficial Herd of Local Gradients","created":"2024-03-25","cited_by_count":0,"area":null},{"id":"2501.02205","title":"Digital Twin Calibration with Model-Based Reinforcement Learning","created":"2025-01-04","cited_by_count":0,"area":"deeprl"},{"id":"2512.11052","title":"An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.05489","title":"Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2605.00281","title":"High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2605.07239","title":"Sample Complexity of Stochastic Optimization with Integer Variables","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2606.24879","title":"New Bounds for the Last Iterate of the Stochastic subGradient Method","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2606.29593","title":"How AI settled the complexity of the oldest SGD algorithm","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2607.10959","title":"WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2608.27313","title":"A Finite-Sample Analysis of Quantile Temporal-Difference Learning","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2609.12765","title":"High-Probability Convergence of SGD via Batched Updates","created":"2026-09-11","cited_by_count":0,"area":null},{"id":"2609.32729","title":"Revisiting AdaGrad in Stochastic Convex Optimization: Last Iterates, High Probability, and Lower Bounds","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.37425","title":"A stochastic subgradient method with optimal failure exponent","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.06070","title":"Last-Iterate Convergence Rate of Normalized Gradient Descent under H\\\"older Smoothness","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.16923","title":"Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.19444","title":"Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}