Stochastic Methods

Optimal Learning for Multi-pass Stochastic Gradient Methods NeurIPS 2016 Optimal Tagging with Markov Chain Optimization NeurIPS 2016 PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem ICML 2016 Pairwise Matching Through Max-Weight Bipartite Belief Propagation CVPR 2016 Pliable Rejection Sampling ICML 2016 Probabilistic Linear Multistep Methods NeurIPS 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NeurIPS 2016 Pseudo-Marginal Slice Sampling AISTATS 2016 Quantized Random Projections and Non-Linear Estimation of Cosine Similarity NeurIPS 2016 Quantum Perceptron Models NeurIPS 2016 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels JMLR 2016 Random Features for Sparse Signal Classification CVPR 2016 Recycling Randomness with Structure for Sublinear time Kernel Expansions ICML 2016 Regret of Queueing Bandits NeurIPS 2016 Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics JMLR 2016 SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization ICML 2016 Shifting Regret, Mirror Descent, and Matrices ICML 2016 Simple and Efficient Weighted Minwise Hashing NeurIPS 2016 Social LSTM: Human Trajectory Prediction in Crowded Spaces CVPR 2016 SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling NeurIPS 2016 Sparse Parameter Recovery from Aggregated Data ICML 2016 Spectral Learning of Dynamic Systems from Nonequilibrium Data NeurIPS 2016 Starting Small - Learning with Adaptive Sample Sizes ICML 2016 Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences NeurIPS 2016 Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo NeurIPS 2016