Stochastic Methods

On Testing of Uniform Samplers AAAI 2019 On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019 On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes AISTATS 2019 On the Global Convergence of (Fast) Incremental Expectation Maximization Methods NeurIPS 2019 On the Ineffectiveness of Variance Reduced Optimization for Deep Learning NeurIPS 2019 Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019 Optimal Mini-Batch and Step Sizes for SAGA ICML 2019 Optimal Stochastic and Online Learning with Individual Iterates NeurIPS 2019 Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering NeurIPS 2019 Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning AAAI 2019 Pathwise Derivatives for Multivariate Distributions AISTATS 2019 Periodic Kernel Approximation by Index Set Fourier Series Features UAI 2019 Problem-dependent Regret Bounds for Online Learning with Feedback Graphs UAI 2019 Proximal Splitting Meets Variance Reduction AISTATS 2019 Pseudo-Extended Markov chain Monte Carlo NeurIPS 2019 Randomized Iterative Algorithms for Fisher Discriminant Analysis UAI 2019 Rao-Blackwellized Stochastic Gradients for Discrete Distributions ICML 2019 Reducing the variance in online optimization by transporting past gradients NeurIPS 2019 Risk-Averse Stochastic Convex Bandit AISTATS 2019 Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019 Scalable Semi-Supervised SVM via Triply Stochastic Gradients IJCAI 2019 Selective Sampling-based Scalable Sparse Subspace Clustering NeurIPS 2019 SpiderBoost and Momentum: Faster Variance Reduction Algorithms NeurIPS 2019 Stein Variational Gradient Descent With Matrix-Valued Kernels NeurIPS 2019 Stochastic Canonical Correlation Analysis JMLR 2019