saddle-point problems

Differentially private algorithms for linear queries via stochastic convex optimization AISTATS 2025 Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract COLT 2024 Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure NeurIPS 2023 Stochastic Approximation Approaches to Group Distributionally Robust Optimization NeurIPS 2023 Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling NeurIPS 2022 A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators JMLR 2022 Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox NeurIPS 2022 Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving NeurIPS 2021 Distributed Saddle-Point Problems Under Data Similarity NeurIPS 2021 First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems JMLR 2021 Increasing Iterate Averaging for Solving Saddle-Point Problems AAAI 2021 Variational Bayesian Optimistic Sampling NeurIPS 2021 Dual Instrumental Variable Regression NeurIPS 2020 Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling NeurIPS 2020 Linear Lower Bounds and Conditioning of Differentiable Games ICML 2020 Revisiting Stochastic Extragradient AISTATS 2020 Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile ICLR 2019 Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization NeurIPS 2018 Stochastic Variance Reduction Methods for Policy Evaluation ICML 2017 Stochastic Variance Reduction Methods for Saddle-Point Problems NeurIPS 2016 Optimization, Learning, and Games with Predictable Sequences NeurIPS 2013