Learning Theory

A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin JMLR 2011 A Reinforcement Learning Theory for Homeostatic Regulation NeurIPS 2011 Autonomous Learning of Action Models for Planning NeurIPS 2011 Better Algorithms for Benign Bandits JMLR 2011 Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization COLT 2011 Blackwell Approachability and No-Regret Learning are Equivalent COLT 2011 Bounds on Individual Risk for Log-loss Predictors COLT 2011 Competitive Closeness Testing COLT 2011 Complexity-Based Approach to Calibration with Checking Rules COLT 2011 Contextual Bandit Algorithms with Supervised Learning Guarantees AISTATS 2011 Contextual Bandits with Linear Payoff Functions AISTATS 2011 Discussion of “Contextual Bandit Algorithms with Supervised Learning Guarantees” AISTATS 2011 Distribution-Independent Evolvability of Linear Threshold Functions COLT 2011 Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression NeurIPS 2011 Error Analysis of Laplacian Eigenmaps for Semi-supervised Learning AISTATS 2011 Finite Time Analysis of Stratified Sampling for Monte Carlo NeurIPS 2011 From Bandits to Experts: On the Value of Side-Observations NeurIPS 2011 Generalization Bound for Infinitely Divisible Empirical Process AISTATS 2011 Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss NeurIPS 2011 Greedy Algorithms for Structurally Constrained High Dimensional Problems NeurIPS 2011 Greedy Model Averaging NeurIPS 2011 Identifiability of Priors from Bounded Sample Sizes with Applications to Transfer Learning COLT 2011 Improved Algorithms for Linear Stochastic Bandits NeurIPS 2011 Improved Loss Bounds For Multiple Kernel Learning AISTATS 2011 Information, Divergence and Risk for Binary Experiments JMLR 2011