{"total":135,"rows":[{"id":"2102.13179","title":"Machine Unlearning via Algorithmic Stability","created":"2021-02-25","cited_by_count":130,"area":"safety"},{"id":"2205.09588","title":"How catastrophic can catastrophic forgetting be in linear regression?","created":"2022-05-19","cited_by_count":81,"area":null},{"id":"2102.11845","title":"Learning with User-Level Privacy","created":"2021-02-23","cited_by_count":72,"area":null},{"id":"2206.05825","title":"A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games","created":"2022-06-12","cited_by_count":66,"area":null},{"id":"2102.08352","title":"Stochastic Variance Reduction for Variational Inequality Methods","created":"2021-02-16","cited_by_count":47,"area":null},{"id":"2406.01581","title":"Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit","created":"2024-06-03","cited_by_count":46,"area":null},{"id":"2302.06763","title":"Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise","created":"2023-02-14","cited_by_count":27,"area":null},{"id":"2306.03824","title":"Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters","created":"2023-06-06","cited_by_count":27,"area":null},{"id":"2106.09481","title":"Stochastic Bias-Reduced Gradient Methods","created":"2021-06-17","cited_by_count":25,"area":null},{"id":"2107.09461","title":"CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression","created":"2021-07-20","cited_by_count":23,"area":null},{"id":"2206.00363","title":"Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization","created":"2022-06-01","cited_by_count":22,"area":null},{"id":"2406.15244","title":"AdaGrad under Anisotropic Smoothness","created":"2024-06-21","cited_by_count":22,"area":null},{"id":"2102.07245","title":"Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization","created":"2021-02-14","cited_by_count":21,"area":null},{"id":"2108.04755","title":"FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning","created":"2021-08-10","cited_by_count":20,"area":null},{"id":"2203.09436","title":"Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions","created":"2022-03-17","cited_by_count":19,"area":null},{"id":"2206.01029","title":"Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions","created":"2022-06-02","cited_by_count":19,"area":null},{"id":"2209.02257","title":"Faster federated optimization under second-order similarity","created":"2022-09-06","cited_by_count":18,"area":null},{"id":"2105.01778","title":"Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss","created":"2021-05-04","cited_by_count":17,"area":null},{"id":"2109.04522","title":"Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees","created":"2021-09-09","cited_by_count":17,"area":null},{"id":"2202.00308","title":"PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation","created":"2022-02-01","cited_by_count":17,"area":null},{"id":"2106.01761","title":"Near Optimal Stochastic Algorithms for Finite-Sum Unbalanced Convex-Concave Minimax Optimization","created":"2021-06-03","cited_by_count":16,"area":null},{"id":"2106.03696","title":"Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models","created":"2021-06-07","cited_by_count":16,"area":null},{"id":"2106.11938","title":"Robust Regression Revisited: Acceleration and Improved Estimation Rates","created":"2021-06-22","cited_by_count":15,"area":null},{"id":"2110.03313","title":"Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees","created":"2021-10-07","cited_by_count":15,"area":null},{"id":"2202.04640","title":"Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods","created":"2022-02-09","cited_by_count":15,"area":null},{"id":"2211.01851","title":"Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization","created":"2022-11-03","cited_by_count":15,"area":null},{"id":"2105.07291","title":"Adaptive Newton Sketch: Linear-time Optimization with Quadratic Convergence and Effective Hessian Dimensionality","created":"2021-05-15","cited_by_count":14,"area":null},{"id":"2203.13225","title":"Distributionally Robust Optimization via Ball Oracle Acceleration","created":"2022-03-24","cited_by_count":14,"area":null},{"id":"2102.09234","title":"ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks","created":"2021-02-18","cited_by_count":13,"area":null},{"id":"2102.09645","title":"SVRG Meets AdaGrad: Painless Variance Reduction","created":"2021-02-18","cited_by_count":13,"area":null},{"id":"2206.07553","title":"On the fast convergence of minibatch heavy ball momentum","created":"2022-06-15","cited_by_count":13,"area":null},{"id":"2206.03588","title":"Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation","created":"2022-06-07","cited_by_count":11,"area":null},{"id":"2304.07504","title":"Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis","created":"2023-04-15","cited_by_count":11,"area":null},{"id":"2310.02987","title":"Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions","created":"2023-10-04","cited_by_count":11,"area":null},{"id":"2102.13643","title":"Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums","created":"2021-02-26","cited_by_count":10,"area":null},{"id":"2111.01842","title":"Coordinate Linear Variance Reduction for Generalized Linear Programming","created":"2021-11-02","cited_by_count":10,"area":null},{"id":"2101.11041","title":"Complementary Composite Minimization, Small Gradients in General Norms, and Applications","created":"2021-01-26","cited_by_count":9,"area":null},{"id":"2103.11333","title":"ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method","created":"2021-03-21","cited_by_count":9,"area":null},{"id":"2203.01744","title":"Accelerated SGD for Non-Strongly-Convex Least Squares","created":"2022-03-03","cited_by_count":9,"area":null},{"id":"2206.02702","title":"Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches","created":"2022-06-06","cited_by_count":9,"area":null},{"id":"2212.05088","title":"Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization","created":"2022-12-09","cited_by_count":9,"area":null},{"id":"2310.11291","title":"An Automatic Learning Rate Schedule Algorithm for Achieving Faster Convergence and Steeper Descent","created":"2023-10-17","cited_by_count":9,"area":null},{"id":"2110.03294","title":"EF21 with Bells & Whistles: Six Algorithmic Extensions of Modern Error Feedback","created":"2021-10-07","cited_by_count":8,"area":null},{"id":"2110.09057","title":"Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization","created":"2021-10-18","cited_by_count":8,"area":null},{"id":"2206.08627","title":"RECAPP: Crafting a More Efficient Catalyst for Convex Optimization","created":"2022-06-17","cited_by_count":8,"area":null},{"id":"2211.08597","title":"SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates","created":"2022-11-16","cited_by_count":8,"area":null},{"id":"2103.12293","title":"Stochastic Reweighted Gradient Descent","created":"2021-03-23","cited_by_count":7,"area":null},{"id":"2111.13322","title":"Random-reshuffled SARAH does not need a full gradient computations","created":"2021-11-26","cited_by_count":7,"area":null},{"id":"2112.14332","title":"Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback","created":"2021-12-28","cited_by_count":7,"area":null},{"id":"2208.05287","title":"Adaptive Learning Rates for Faster Stochastic Gradient Methods","created":"2022-08-10","cited_by_count":7,"area":null},{"id":"2301.09675","title":"Improved Rate of First Order Algorithms for Entropic Optimal Transport","created":"2023-01-23","cited_by_count":7,"area":null},{"id":"2304.11737","title":"Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features","created":"2023-04-23","cited_by_count":7,"area":null},{"id":"2306.14522","title":"Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning","created":"2023-06-26","cited_by_count":7,"area":null},{"id":"2311.05589","title":"A Coefficient Makes SVRG Effective","created":"2023-11-09","cited_by_count":7,"area":null},{"id":"2405.05818","title":"Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems","created":"2024-05-09","cited_by_count":7,"area":null},{"id":"2201.07684","title":"On the Complexity of a Practical Primal-Dual Coordinate Method","created":"2022-01-19","cited_by_count":6,"area":null},{"id":"2203.00813","title":"An Accelerated Stochastic Variance-Reduced Algorithm for Entropic Wasserstein Barycenters","created":"2022-03-02","cited_by_count":6,"area":null},{"id":"2208.10025","title":"Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization","created":"2022-08-22","cited_by_count":5,"area":null},{"id":"2210.16400","title":"Flatter, faster: scaling momentum for optimal speedup of SGD","created":"2022-10-28","cited_by_count":5,"area":null},{"id":"2101.00935","title":"First-Order Methods for Convex Optimization","created":"2021-01-04","cited_by_count":4,"area":null},{"id":"2105.12062","title":"Practical Schemes for Finding Near-Stationary Points of Convex Finite-Sums","created":"2021-05-25","cited_by_count":4,"area":null},{"id":"2110.10093","title":"Stochastic Primal-Dual Deep Unrolling","created":"2021-10-19","cited_by_count":4,"area":null},{"id":"2201.12302","title":"Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction","created":"2022-01-28","cited_by_count":4,"area":null},{"id":"2211.14645","title":"Accelerated Riemannian Optimization: Handling Constraints with a Prox to Bound Geometric Penalties","created":"2022-11-26","cited_by_count":4,"area":null},{"id":"2301.03125","title":"Sharper Analysis for Minibatch Stochastic Proximal Point Methods: Stability, Smoothness, and Deviation","created":"2023-01-09","cited_by_count":4,"area":null},{"id":"2311.11166","title":"From Optimization to Control: Quasi Policy Iteration","created":"2023-11-18","cited_by_count":4,"area":null},{"id":"2406.03006","title":"Quantum Algorithms and Lower Bounds for Finite-Sum Optimization","created":"2024-06-05","cited_by_count":4,"area":null},{"id":"2501.15941","title":"SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning","created":"2025-01-27","cited_by_count":4,"area":null},{"id":"2503.08427","title":"Accelerated Distributed Optimization with Compression and Error Feedback","created":"2025-03-11","cited_by_count":4,"area":null},{"id":"2505.08146","title":"Tensor Sketch: Fast and Scalable Polynomial Kernel Approximation","created":"2025-05-13","cited_by_count":4,"area":null},{"id":"2505.08376","title":"Adaptive Diffusion Policy Optimization for Robotic Manipulation","created":"2025-05-13","cited_by_count":4,"area":"diffusion"},{"id":"2608.12043","title":"Direct Acceleration of Stochastic Root-Finding Without Variance Reduction and Regularization","created":"2026-08-12","cited_by_count":4,"area":null},{"id":"2202.00509","title":"Decentralized Stochastic Variance Reduced Extragradient Method","created":"2022-02-01","cited_by_count":3,"area":null},{"id":"2203.12191","title":"An Adaptive Gradient Method with Energy and Momentum","created":"2022-03-23","cited_by_count":3,"area":null},{"id":"2303.16279","title":"Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization","created":"2023-03-28","cited_by_count":3,"area":null},{"id":"2401.05518","title":"Correlated Quantization for Faster Nonconvex Distributed Optimization","created":"2024-01-10","cited_by_count":3,"area":null},{"id":"2404.14758","title":"Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients","created":"2024-04-23","cited_by_count":3,"area":null},{"id":"2505.07614","title":"Bant: Byzantine Antidote via Trial Function and Trust Scores","created":"2025-05-12","cited_by_count":3,"area":null},{"id":"2101.01505","title":"Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications","created":"2021-01-05","cited_by_count":2,"area":null},{"id":"2104.02388","title":"Generalization of GANs and overparameterized models under Lipschitz continuity","created":"2021-04-06","cited_by_count":2,"area":null},{"id":"2106.05445","title":"Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach","created":"2021-06-10","cited_by_count":2,"area":null},{"id":"2206.00529","title":"Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top","created":"2022-06-01","cited_by_count":2,"area":null},{"id":"2211.15310","title":"Stochastic Steffensen method","created":"2022-11-28","cited_by_count":2,"area":null},{"id":"2212.06283","title":"Variance-Reduced Conservative Policy Iteration","created":"2022-12-12","cited_by_count":2,"area":null},{"id":"2402.00219","title":"FedCore: Straggler-Free Federated Learning with Distributed Coresets","created":"2024-01-31","cited_by_count":2,"area":null},{"id":"2402.02569","title":"On the Complexity of Finite-Sum Smooth Optimization under the Polyak-{\\L}ojasiewicz Condition","created":"2024-02-04","cited_by_count":2,"area":null},{"id":"2403.06677","title":"Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction","created":"2024-03-11","cited_by_count":2,"area":null},{"id":"2409.14280","title":"Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning","created":"2024-09-22","cited_by_count":2,"area":null},{"id":"2412.16414","title":"Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity","created":"2024-12-21","cited_by_count":2,"area":null},{"id":"2502.14648","title":"Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling","created":"2025-02-20","cited_by_count":2,"area":null},{"id":"2503.03023","title":"Quantum Non-Linear Bandit Optimization","created":"2025-03-04","cited_by_count":2,"area":null},{"id":"2506.16283","title":"Random feature approximation for general spectral methods","created":"2025-06-19","cited_by_count":2,"area":null},{"id":"2507.11724","title":"Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure","created":"2025-07-15","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":"2101.02397","title":"A Comprehensive Study on Optimization Strategies for Gradient Descent In Deep Learning","created":"2021-01-07","cited_by_count":1,"area":null},{"id":"2106.11541","title":"Kernel Clustering with Sigmoid-based Regularization for Efficient Segmentation of Sequential Data","created":"2021-06-22","cited_by_count":1,"area":null},{"id":"2201.13387","title":"L-SVRG and L-Katyusha with Adaptive Sampling","created":"2022-01-31","cited_by_count":1,"area":null},{"id":"2202.05062","title":"Equivariance Regularization for Image Reconstruction","created":"2022-02-10","cited_by_count":1,"area":null},{"id":"2205.00058","title":"Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent","created":"2022-04-29","cited_by_count":1,"area":null},{"id":"2205.10247","title":"SADAM: Stochastic Adam, A Stochastic Operator for First-Order Gradient-based Optimizer","created":"2022-05-20","cited_by_count":1,"area":null}],"next_cursor":"100"}