Learning Theory

Dimension-Free Iteration Complexity of Finite Sum Optimization Problems NeurIPS 2016 Efficient approaches for escaping higher order saddle points in non-convex optimization COLT 2016 Estimating Diffusion Networks: Recovery Conditions, Sample Complexity and Soft-thresholding Algorithm JMLR 2016 Fast learning rates with heavy-tailed losses NeurIPS 2016 Finite Sample Prediction and Recovery Bounds for Ordinal Embedding NeurIPS 2016 Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities NeurIPS 2016 Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case JMLR 2016 Generalization and Exploration via Randomized Value Functions ICML 2016 Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back NeurIPS 2016 Generalization Properties and Implicit Regularization for Multiple Passes SGM ICML 2016 Global Optimality of Local Search for Low Rank Matrix Recovery NeurIPS 2016 Graph Connectivity in Noisy Sparse Subspace Clustering AISTATS 2016 Group Equivariant Convolutional Networks ICML 2016 Hardness of Online Sleeping Combinatorial Optimization Problems NeurIPS 2016 Improving PAC Exploration Using the Median Of Means NeurIPS 2016 Instance-dependent Regret Bounds for Dueling Bandits COLT 2016 Interactive Algorithms: from Pool to Stream COLT 2016 Iterative Regularization for Learning with Convex Loss Functions JMLR 2016 L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs JMLR 2016 L1-regularized Neural Networks are Improperly Learnable in Polynomial Time ICML 2016 Learnability of Non-I.I.D. ACML 2016 Learning Bound for Parameter Transfer Learning NeurIPS 2016 Learning Combinatorial Functions from Pairwise Comparisons COLT 2016 Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers ACML 2016 Learning in Games: Robustness of Fast Convergence NeurIPS 2016