Stochastic Methods

Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization ICLR 2019 Stochastic Negative Mining for Learning with Large Output Spaces AISTATS 2019 Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NeurIPS 2019 Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NeurIPS 2019 SySCD: A System-Aware Parallel Coordinate Descent Algorithm NeurIPS 2019 Tensor Monte Carlo: Particle Methods for the GPU era NeurIPS 2019 Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning NeurIPS 2019 The Randomized Midpoint Method for Log-Concave Sampling NeurIPS 2019 Towards closing the gap between the theory and practice of SVRG NeurIPS 2019 Toward Understanding the Impact of Staleness in Distributed Machine Learning ICLR 2019 Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples NeurIPS 2019 Understanding the Role of Momentum in Stochastic Gradient Methods NeurIPS 2019 Unifying Orthogonal Monte Carlo Methods ICML 2019 User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning NeurIPS 2019 Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines AAAI 2019 D^2: Decentralized Training over Decentralized Data ICML 2018 Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation EMNLP 2018 Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy AISTATS 2018 A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates ICML 2018 Asymptotic optimality of adaptive importance sampling NeurIPS 2018 Asynchronous Byzantine Machine Learning (the case of SGD) ICML 2018 Averaging Stochastic Gradient Descent on Riemannian Manifolds COLT 2018 Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation NeurIPS 2018 Byzantine Stochastic Gradient Descent NeurIPS 2018 Contextual Bandits with Stochastic Experts AISTATS 2018