stochastic optimization

Hierarchical Decision Making In Electricity Grid Management ICML 2016 Importance Sampling Tree for Large-scale Empirical Expectation ICML 2016 Improved Dropout for Shallow and Deep Learning NeurIPS 2016 Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives ICML 2016 Incremental Variational Sparse Gaussian Process Regression NeurIPS 2016 LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain NeurIPS 2016 Learning Deep Embeddings with Histogram Loss NeurIPS 2016 Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods NeurIPS 2016 Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption JMLR 2016 Local Minimax Complexity of Stochastic Convex Optimization NeurIPS 2016 NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization NeurIPS 2016 One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities NeurIPS 2016 Online learning in repeated auctions COLT 2016 Online Learning with Feedback Graphs Without the Graphs ICML 2016 Online Stochastic Linear Optimization under One-bit Feedback ICML 2016 Optimistic Bandit Convex Optimization NeurIPS 2016 PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem ICML 2016 Principled Parallel Mean-Field Inference for Discrete Random Fields CVPR 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NeurIPS 2016 Regret of Queueing Bandits NeurIPS 2016 Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds NeurIPS 2016 SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization ICML 2016 SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques NeurIPS 2016 Simultaneous Clustering and Model Selection for Tensor Affinities CVPR 2016 Stochastic Block BFGS: Squeezing More Curvature out of Data ICML 2016