learning theory

Bridging Supervised Learning and Test-Based Co-optimization JMLR 2017 Convergence of Gradient EM on Multi-component Mixture of Gaussians NeurIPS 2017 Diverse Neural Network Learns True Target Functions AISTATS 2017 Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks NeurIPS 2017 Generalization Error of Invariant Classifiers AISTATS 2017 Information-theoretic analysis of generalization capability of learning algorithms NeurIPS 2017 Learning Overcomplete HMMs NeurIPS 2017 Learning ReLUs via Gradient Descent NeurIPS 2017 Lower bounds on the robustness to adversarial perturbations NeurIPS 2017 Nearly-tight VC-dimension bounds for piecewise linear neural networks COLT 2017 Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem NeurIPS 2017 New bounds on the price of bandit feedback for mistake-bounded online multiclass learning ALT 2017 Nonparametric Risk Bounds for Time-Series Forecasting JMLR 2017 On Structured Prediction Theory with Calibrated Convex Surrogate Losses NeurIPS 2017 On the Complexity of Learning Neural Networks NeurIPS 2017 On the Learnability of Fully-Connected Neural Networks AISTATS 2017 Open Problem: First-Order Regret Bounds for Contextual Bandits COLT 2017 Preference-based Teaching JMLR 2017 Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes COLT 2017 Regularization and the small-ball method II: complexity dependent error rates JMLR 2017 SGD Learns the Conjugate Kernel Class of the Network NeurIPS 2017 Understanding How Feature Structure Transfers in Transfer Learning IJCAI 2017 Uniform Deviation Bounds for k-Means Clustering ICML 2017 Barron and Cover’s Theory in Supervised Learning and its Application to Lasso ICML 2016 Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees ACML 2016