Learning Theory

Learning and the Unknown: Surveying Steps toward Open World Recognition AAAI 2019 Learning-Based Low-Rank Approximations NeurIPS 2019 Learning Deterministic Weighted Automata with Queries and Counterexamples NeurIPS 2019 Learning Distributions Generated by One-Layer ReLU Networks NeurIPS 2019 Learning from Weakly Dependent Data under Dobrushin’s Condition COLT 2019 Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm AISTATS 2019 Learning Neural Networks with Two Nonlinear Layers in Polynomial Time COLT 2019 Learning One-hidden-layer Neural Networks under General Input Distributions AISTATS 2019 Learning Set Functions with Limited Complementarity AAAI 2019 Learning to Collaborate in Markov Decision Processes ICML 2019 Learning-to-Learn Stochastic Gradient Descent with Biased Regularization ICML 2019 Learning to Sample Hard Instances for Graph Algorithms ACML 2019 Learning to Screen NeurIPS 2019 Learning Two-layer Neural Networks with Symmetric Inputs ICLR 2019 Learning Two Layer Rectified Neural Networks in Polynomial Time COLT 2019 Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin AAAI 2019 Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models ICML 2019 Lifelong Optimization with Low Regret AISTATS 2019 Limitations of Lazy Training of Two-layers Neural Network NeurIPS 2019 Limiting Extrapolation in Linear Approximate Value Iteration NeurIPS 2019 Limit Learning Equivalence Structures ALT 2019 Limits of Private Learning with Access to Public Data NeurIPS 2019 List-decodable Linear Regression NeurIPS 2019 Locally Private Learning without Interaction Requires Separation NeurIPS 2019 Logarithmic Regret for Online Control NeurIPS 2019