Learning Theory

On Asymptotic and Finite-Time Optimality of Bayesian Predictors JMLR 2019 On Communication Complexity of Classification Problems COLT 2019 On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits UAI 2019 On Kernel Derivative Approximation with Random Fourier Features AISTATS 2019 On Learning Graphs with Edge-Detecting Queries ALT 2019 On Learning Invariant Representations for Domain Adaptation ICML 2019 On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective NeurIPS 2019 Online EXP3 Learning in Adversarial Bandits with Delayed Feedback NeurIPS 2019 Online Forecasting of Total-Variation-bounded Sequences NeurIPS 2019 Online Learning in Kernelized Markov Decision Processes AISTATS 2019 Online Learning to Rank with Features ICML 2019 On Mean Estimation for General Norms with Statistical Queries COLT 2019 On Network Design Spaces for Visual Recognition ICCV 2019 On Robustness of Principal Component Regression NeurIPS 2019 On Robustness to Adversarial Examples and Polynomial Optimization NeurIPS 2019 On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons NeurIPS 2019 On Structure Priors for Learning Bayesian Networks AISTATS 2019 On the Computational Power of Online Gradient Descent COLT 2019 On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition AISTATS 2019 On the Convergence Rate of Training Recurrent Neural Networks NeurIPS 2019 On the Expressive Power of Deep Polynomial Neural Networks NeurIPS 2019 On the Generalization Gap in Reparameterizable Reinforcement Learning ICML 2019 On the Hardness of Robust Classification NeurIPS 2019 On the Interaction Effects Between Prediction and Clustering AISTATS 2019 On the Limits of Learning to Actively Learn Semantic Representations CoNLL 2019