Learning Theory

The Blinded Bandit: Learning with Adaptive Feedback NeurIPS 2014 The Complexity of Learning Halfspaces using Generalized Linear Methods COLT 2014 The limits of squared Euclidean distance regularization NeurIPS 2014 The sample complexity of agnostic learning under deterministic labels COLT 2014 Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees AISTATS 2014 Time--Data Tradeoffs by Aggressive Smoothing NeurIPS 2014 Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms ICML 2014 Achievability of Asymptotic Minimax Regret in Online and Batch Prediction ACML 2013 Active and passive learning of linear separators under log-concave distributions COLT 2013 Activized Learning with Uniform Classification Noise ICML 2013 Adaptivity to Local Smoothness and Dimension in Kernel Regression NeurIPS 2013 A Gang of Bandits NeurIPS 2013 A Latent Source Model for Nonparametric Time Series Classification NeurIPS 2013 Almost Optimal Exploration in Multi-Armed Bandits ICML 2013 Analyzing the Harmonic Structure in Graph-Based Learning NeurIPS 2013 A Spectral Learning Approach to Range-Only SLAM ICML 2013 A Stability-based Validation Procedure for Differentially Private Machine Learning NeurIPS 2013 Better Rates for Any Adversarial Deterministic MDP ICML 2013 Cluster Trees on Manifolds NeurIPS 2013 Collective Stability in Structured Prediction: Generalization from One Example ICML 2013 Competing with an Infinite Set of Models in Reinforcement Learning AISTATS 2013 Complexity Theoretic Lower Bounds for Sparse Principal Component Detection COLT 2013 Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models NeurIPS 2013 Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems JMLR 2013 Consistency of Online Random Forests ICML 2013