stochastic method

Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions NeurIPS 2021 Numerical Composition of Differential Privacy NeurIPS 2021 Online DR-Submodular Maximization: Minimizing Regret and Constraint Violation AAAI 2021 Revisiting Projection-free Online Learning: the Strongly Convex Case AISTATS 2021 Wyner-Ziv Estimators: Efficient Distributed Mean Estimation with Side-Information AISTATS 2021 Acceleration with a Ball Optimization Oracle NeurIPS 2020 An Investigation Into the Stochasticity of Batch Whitening CVPR 2020 Bayesian Graph Neural Networks with Adaptive Connection Sampling ICML 2020 Dynamic Regret of Convex and Smooth Functions NeurIPS 2020 Large-Scale Methods for Distributionally Robust Optimization NeurIPS 2020 Online Convex Optimization Over Erdos-Renyi Random Networks NeurIPS 2020 On Testing of Samplers NeurIPS 2020 Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses NeurIPS 2020 Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models EMNLP 2020 WONDER: Weighted One-shot Distributed Ridge Regression in High Dimensions JMLR 2020 Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View AAAI 2019 Efficiently avoiding saddle points with zero order methods: No gradients required NeurIPS 2019 Efficient online learning with kernels for adversarial large scale problems NeurIPS 2019 Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication AISTATS 2019 Nonstochastic Multiarmed Bandits with Unrestricted Delays NeurIPS 2019 Real-Time Planning as Decision-Making under Uncertainty AAAI 2019 RSN: Randomized Subspace Newton NeurIPS 2019 Sample Adaptive MCMC NeurIPS 2019 Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions COLT 2019 Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NeurIPS 2019