Learning Theory

On the Characterization of a Class of Fisher-Consistent Loss Functions and its Application to Boosting JMLR 2016 On the Consistency of Feature Selection With Lasso for Non-linear Targets ICML 2016 On the Power and Limits of Distance-Based Learning ICML 2016 On the Recursive Teaching Dimension of VC Classes NeurIPS 2016 Open Problem: Best Arm Identification: Almost Instance-Wise Optimality and the Gap Entropy Conjecture COLT 2016 Open Problem: Second order regret bounds based on scaling time COLT 2016 Optimal Architectures in a Solvable Model of Deep Networks NeurIPS 2016 Optimal Learning Rates for Localized SVMs JMLR 2016 Optimal Learning via the Fourier Transform for Sums of Independent Integer Random Variables COLT 2016 Optimistic Bandit Convex Optimization NeurIPS 2016 PAC-Bayesian Bounds based on the Rényi Divergence AISTATS 2016 PAC-Bayesian Theory Meets Bayesian Inference NeurIPS 2016 PAC learning of Probabilistic Automaton based on the Method of Moments ICML 2016 PAC Lower Bounds and Efficient Algorithms for The Max K-Armed Bandit Problem ICML 2016 PAC Reinforcement Learning with Rich Observations NeurIPS 2016 Parameter-Free Convex Learning through Coin Betting AutoML 2016 Preference-based Teaching COLT 2016 Pricing a Low-regret Seller ICML 2016 Properly Learning Poisson Binomial Distributions in Almost Polynomial Time COLT 2016 Pure Exploration of Multi-armed Bandit Under Matroid Constraints COLT 2016 Quantum Perceptron Models NeurIPS 2016 Refined Error Bounds for Several Learning Algorithms JMLR 2016 Refined Lower Bounds for Adversarial Bandits NeurIPS 2016 Regret Bounds for Non-decomposable Metrics with Missing Labels NeurIPS 2016 Regret of Queueing Bandits NeurIPS 2016