Mathematics & Optimization

Structure Learning of Partitioned Markov Networks ICML 2016 Sublabel-Accurate Relaxation of Nonconvex Energies CVPR 2016 Sublinear Time Orthogonal Tensor Decomposition NeurIPS 2016 Sub-sampled Newton Methods with Non-uniform Sampling NeurIPS 2016 Subspace Clustering With Priors via Sparse Quadratically Constrained Quadratic Programming CVPR 2016 Supervised Learning with Tensor Networks NeurIPS 2016 Synergy of Monotonic Rules JMLR 2016 Temporal Epipolar Regions CVPR 2016 Tensor Decomposition via Joint Matrix Schur Decomposition ICML 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 Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations AISTATS 2016 The Chordal Graph Polytope for Learning Decomposable Models PGM 2016 The Constrained Dantzig Selector with Enhanced Consistency JMLR 2016 The Effect of Combination Functions on the Complexity of Relational Bayesian Networks PGM 2016 The Information-Theoretic Requirements of Subspace Clustering with Missing Data ICML 2016 The Multi-fidelity Multi-armed Bandit NeurIPS 2016 The Multiscale Laplacian Graph Kernel NeurIPS 2016 The Next Best Underwater View CVPR 2016 The non-convex Burer-Monteiro approach works on smooth semidefinite programs NeurIPS 2016 The Optimal Sample Complexity of PAC Learning JMLR 2016 The Parallel Knowledge Gradient Method for Batch Bayesian Optimization NeurIPS 2016 The Parameterized Complexity of Approximate Inference in Bayesian Networks PGM 2016 The Power of Optimization from Samples NeurIPS 2016 The Product Cut NeurIPS 2016