Stochastic Methods

Stein Self-Repulsive Dynamics: Benefits From Past Samples NeurIPS 2020 Stochastically Dominant Distributional Reinforcement Learning ICML 2020 Stochastic Approximate Gradient Descent via the Langevin Algorithm AAAI 2020 Stochastic bandits with arm-dependent delays ICML 2020 Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization JMLR 2020 Stochastic Coordinate Minimization with Progressive Precision for Stochastic Convex Optimization ICML 2020 Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization ICML 2020 Stochastic Gradient and Langevin Processes ICML 2020 Stochastic Hamiltonian Gradient Methods for Smooth Games ICML 2020 Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function NeurIPS 2020 Stochastic Online Learning with Probabilistic Graph Feedback AAAI 2020 Stochastic Optimization for Non-convex Inf-Projection Problems ICML 2020 Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping NeurIPS 2020 Stochastic Optimization with Laggard Data Pipelines NeurIPS 2020 StochasticRank: Global Optimization of Scale-Free Discrete Functions ICML 2020 Stochastic Recursive Variance-Reduced Cubic Regularization Methods AISTATS 2020 Stochastic Stein Discrepancies NeurIPS 2020 Streaming k-Submodular Maximization under Noise subject to Size Constraint ICML 2020 Structure Adaptive Algorithms for Stochastic Bandits ICML 2020 Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis ICML 2020 Sublinear Optimal Policy Value Estimation in Contextual Bandits AISTATS 2020 Sub-sampling for Efficient Non-Parametric Bandit Exploration NeurIPS 2020 Subset Selection by Pareto Optimization with Recombination AAAI 2020 SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm NeurIPS 2020 SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence NeurIPS 2020