Stochastic Methods

Natural Evolution Strategies JMLR 2014 New Bounds on Compressive Linear Least Squares Regression AISTATS 2014 Nonlinear Information-Theoretic Compressive Measurement Design ICML 2014 On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning AISTATS 2014 Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors AISTATS 2014 Parallel Direction Method of Multipliers NeurIPS 2014 Parallel Feature Selection Inspired by Group Testing NeurIPS 2014 Parallelizing Exploration-Exploitation Tradeoffs in Gaussian Process Bandit Optimization JMLR 2014 Positive Curvature and Hamiltonian Monte Carlo NeurIPS 2014 Projecting Markov Random Field Parameters for Fast Mixing NeurIPS 2014 Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels ICML 2014 Random Laplace Feature Maps for Semigroup Kernels on Histograms CVPR 2014 Realtime and Robust Hand Tracking from Depth CVPR 2014 Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature NeurIPS 2014 Scalable Kernel Methods via Doubly Stochastic Gradients NeurIPS 2014 Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models NeurIPS 2014 Smoothed Gradients for Stochastic Variational Inference NeurIPS 2014 Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space NeurIPS 2014 Spoofing Large Probability Mass Functions to Improve Sampling Times and Reduce Memory Costs AISTATS 2014 Statistical analysis of stochastic gradient methods for generalized linear models ICML 2014 Stereo under Sequential Optimal Sampling: A Statistical Analysis Framework for Search Space Reduction CVPR 2014 Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers ICML 2014 Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm NeurIPS 2014 Stochastic Gradient Hamiltonian Monte Carlo ICML 2014 Stochastic Proximal Gradient Descent with Acceleration Techniques NeurIPS 2014