Mathematics & Optimization

Sparse Support Recovery with Non-smooth Loss Functions NeurIPS 2016 Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes JMLR 2016 Spectral Learning of Dynamic Systems from Nonequilibrium Data NeurIPS 2016 Spectral thresholds in the bipartite stochastic block model COLT 2016 Split LBI: An Iterative Regularization Path with Structural Sparsity NeurIPS 2016 SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions JMLR 2016 Stable Graphical Models JMLR 2016 Stacked Attention Networks for Image Question Answering CVPR 2016 Starting Small - Learning with Adaptive Sample Sizes ICML 2016 Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices JMLR 2016 Statistical Inference for Pairwise Graphical Models Using Score Matching NeurIPS 2016 Stochastic Discrete Clenshaw-Curtis Quadrature ICML 2016 Stochastic Gradient Geodesic MCMC Methods NeurIPS 2016 Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences NeurIPS 2016 Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo NeurIPS 2016 Stochastic Optimization for Large-scale Optimal Transport NeurIPS 2016 Stochastic Three-Composite Convex Minimization NeurIPS 2016 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning ICML 2016 Stochastic Variance Reduction for Nonconvex Optimization ICML 2016 Stochastic Variance Reduction Methods for Saddle-Point Problems NeurIPS 2016 Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja’s Algorithm COLT 2016 Structure-Blind Signal Recovery NeurIPS 2016 Structure Discovery in Bayesian Networks by Sampling Partial Orders JMLR 2016 Structured Matrix Recovery via the Generalized Dantzig Selector NeurIPS 2016 Structured Sparse Regression via Greedy Hard Thresholding NeurIPS 2016