Stochastic Methods

Black-Box Alpha Divergence Minimization ICML 2016 CMA-ES with Optimal Covariance Update and Storage Complexity NeurIPS 2016 Coin Betting and Parameter-Free Online Learning NeurIPS 2016 Cyclades: Conflict-free Asynchronous Machine Learning NeurIPS 2016 Delay and Cooperation in Nonstochastic Bandits COLT 2016 Distributed Clustering of Linear Bandits in Peer to Peer Networks ICML 2016 Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters NeurIPS 2016 Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics JMLR 2016 Fast Rate Analysis of Some Stochastic Optimization Algorithms ICML 2016 Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back NeurIPS 2016 Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares NeurIPS 2016 Improved Dropout for Shallow and Deep Learning NeurIPS 2016 Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016 NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization NeurIPS 2016 Online and Differentially-Private Tensor Decomposition NeurIPS 2016 Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes NeurIPS 2016 Online Stochastic Linear Optimization under One-bit Feedback ICML 2016 Optimal Learning for Multi-pass Stochastic Gradient Methods NeurIPS 2016 Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent NeurIPS 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NeurIPS 2016 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels JMLR 2016 Scalable Adaptive Stochastic Optimization Using Random Projections NeurIPS 2016 Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics JMLR 2016 SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques NeurIPS 2016