stochastic optimization

Continuous Mean-Covariance Bandits NeurIPS 2021 Convergence of adaptive algorithms for constrained weakly convex optimization NeurIPS 2021 Differentiable Quality Diversity NeurIPS 2021 Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings NeurIPS 2021 Direct-Search for a Class of Stochastic Min-Max Problems AISTATS 2021 Distributionally Robust Optimization with Markovian Data ICML 2021 Dual-Mandate Patrols: Multi-Armed Bandits for Green Security AAAI 2021 Dueling Bandits with Team Comparisons NeurIPS 2021 EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback NeurIPS 2021 Efficient debiased evidence estimation by multilevel Monte Carlo sampling UAI 2021 Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods CVPR 2021 ErrorCompensatedX: error compensation for variance reduced algorithms NeurIPS 2021 Evaluating Meta-Reinforcement Learning through a HVAC Control Benchmark (Student Abstract) AAAI 2021 Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach NeurIPS 2021 Fast Learning for Renewal Optimization in Online Task Scheduling JMLR 2021 Fast margin maximization via dual acceleration ICML 2021 Fast Rate Learning in Stochastic First Price Bidding ACML 2021 Fast rates for prediction with limited expert advice NeurIPS 2021 Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights NeurIPS 2021 Fast Stochastic Bregman Gradient Methods: Sharp Analysis and Variance Reduction ICML 2021 From Low Probability to High Confidence in Stochastic Convex Optimization JMLR 2021 Generalization Bounds for Stochastic Saddle Point Problems AISTATS 2021 High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails NeurIPS 2021 High Probability Complexity Bounds for Line Search Based on Stochastic Oracles NeurIPS 2021 How Data Augmentation affects Optimization for Linear Regression NeurIPS 2021