Learning Theory

Learning Kernels with Random Features NeurIPS 2016 Learning Simple Auctions COLT 2016 Learning Taxonomy Adaptation in Large-scale Classification JMLR 2016 Learning Theory for Distribution Regression JMLR 2016 Learning Tree Structured Potential Games NeurIPS 2016 Learning Using Anti-Training with Sacrificial Data JMLR 2016 Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle JMLR 2016 Loss Bounds and Time Complexity for Speed Priors AISTATS 2016 Machine Learning in an Auction Environment JMLR 2016 Memory, Communication, and Statistical Queries COLT 2016 Metric Learning as Convex Combinations of Local Models With Generalization Guarantees CVPR 2016 Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels NeurIPS 2016 Mistake Bounds for Binary Matrix Completion NeurIPS 2016 More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning NeurIPS 2016 Multi-armed Bandits: Competing with Optimal Sequences NeurIPS 2016 Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness JMLR 2016 Multi-scale exploration of convex functions and bandit convex optimization COLT 2016 Multi-step learning and underlying structure in statistical models NeurIPS 2016 Nearly Optimal Classification for Semimetrics AISTATS 2016 No Regret Bound for Extreme Bandits AISTATS 2016 Online Learning with Feedback Graphs Without the Graphs ICML 2016 Online Learning with Noisy Side Observations AISTATS 2016 Online PCA with Optimal Regret JMLR 2016 Online Sparse Linear Regression COLT 2016 Only H is left: Near-tight Episodic PAC RL NeurIPS 2016