Learning Theory

Differentially Private Empirical Risk Minimization Revisited: Faster and More General NeurIPS 2017 Diffusion Approximations for Online Principal Component Estimation and Global Convergence NeurIPS 2017 Discovering Potential Correlations via Hypercontractivity NeurIPS 2017 Discriminative Information Retrieval for Question Answering Sentence Selection EACL 2017 Discriminative State Space Models NeurIPS 2017 Diverse Neural Network Learns True Target Functions AISTATS 2017 Effective Semisupervised Learning on Manifolds COLT 2017 Efficient Kernel Selection via Spectral Analysis IJCAI 2017 Efficient PAC Learning from the Crowd COLT 2017 Efficient Regret Minimization in Non-Convex Games ICML 2017 Efficient, Safe, and Probably Approximately Complete Learning of Action Models IJCAI 2017 Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks NeurIPS 2017 Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds COLT 2017 Erasing Pattern Languages Distinguishable by a Finite Number of Strings ALT 2017 Exploring Generalization in Deep Learning NeurIPS 2017 Failures of Gradient-Based Deep Learning ICML 2017 Fast rates with high probability in exp-concave statistical learning AISTATS 2017 Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities JMLR 2017 Generalization Error of Invariant Classifiers AISTATS 2017 Generalization for Adaptively-chosen Estimators via Stable Median COLT 2017 Generalization Properties of Learning with Random Features NeurIPS 2017 High Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transtition COLT 2017 Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods NeurIPS 2017 Hypotheses testing on infinite random graphs ALT 2017 Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization COLT 2017