Learning Theory

Learning Bounds for Risk-sensitive Learning NeurIPS 2020 Learning From Multi-Dimensional Partial Labels IJCAI 2020 Learning Halfspaces with Massart Noise Under Structured Distributions COLT 2020 Learning in Gated Neural Networks AISTATS 2020 Learning Model with Error -- Exposing the Hidden Model of BAYHENN IJCAI 2020 Learning Near Optimal Policies with Low Inherent Bellman Error ICML 2020 Learning nonlinear dynamical systems from a single trajectory L4DC 2020 Learning Optimal Representations with the Decodable Information Bottleneck NeurIPS 2020 Learning Over-Parametrized Two-Layer Neural Networks beyond NTK COLT 2020 Learning Parities with Neural Networks NeurIPS 2020 Learning piecewise Lipschitz functions in changing environments AISTATS 2020 Learning Polynomials in Few Relevant Dimensions COLT 2020 Learning Restricted Boltzmann Machines with Sparse Latent Variables NeurIPS 2020 Learning Stability Certificates from Data CoRL 2020 Learning Sums of Independent Random Variables with Sparse Collective Support JMLR 2020 Learning the Globally Optimal Distributed LQ Regulator L4DC 2020 Learning to solve the credit assignment problem ICLR 2020 Learning under Model Misspecification: Applications to Variational and Ensemble methods NeurIPS 2020 Learning with Feature and Distribution Evolvable Streams ICML 2020 Learning with Good Feature Representations in Bandits and in RL with a Generative Model ICML 2020 Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium COLT 2020 LIBRE: Learning Interpretable Boolean Rule Ensembles AISTATS 2020 Limits on Testing Structural Changes in Ising Models NeurIPS 2020 Limits to Depth Efficiencies of Self-Attention NeurIPS 2020 Linear-Sample Learning of Low-Rank Distributions NeurIPS 2020