Optimization

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 Kernel Principal Component Analysis AISTATS 2016 Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja’s Algorithm COLT 2016 Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures AISTATS 2016 StructED: Risk Minimization in Structured Prediction JMLR 2016 Structure-Blind Signal Recovery NeurIPS 2016 Structured Feature Similarity With Explicit Feature Map CVPR 2016 Structured Matrix Recovery via the Generalized Dantzig Selector NeurIPS 2016 Structured Prediction Energy Networks ICML 2016 Structured Regression Gradient Boosting CVPR 2016 Structured Sparse Regression via Greedy Hard Thresholding NeurIPS 2016 Structure-Leveraged Methods in Breast Cancer Risk Prediction JMLR 2016 Sublabel-Accurate Relaxation of Nonconvex Energies CVPR 2016 Sub-sampled Newton Methods with Non-uniform Sampling NeurIPS 2016 Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming CVPR 2016 Subspace Detection of DNN Posterior Probabilities via Sparse Representation for Query by Example Spoken Term Detection Interspeech 2016 Tensor Power Iteration for Multi-Graph Matching CVPR 2016 Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization CVPR 2016 TenSR: Multi-Dimensional Tensor Sparse Representation CVPR 2016 The Constrained Dantzig Selector with Enhanced Consistency JMLR 2016 The non-convex Burer-Monteiro approach works on smooth semidefinite programs NeurIPS 2016 The Parallel Knowledge Gradient Method for Batch Bayesian Optimization NeurIPS 2016 The Power of Optimization from Samples NeurIPS 2016