Learning Theory

Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case ICML 2020 Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes JMLR 2020 Finding All ε-Good Arms in Stochastic Bandits NeurIPS 2020 Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent ICML 2020 Fine-Grained Machine Teaching with Attention Modeling AAAI 2020 Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise UAI 2020 Finite-Time Analysis for Double Q-learning NeurIPS 2020 Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise COLT 2020 First-Order Bayesian Regret Analysis of Thompson Sampling ALT 2020 Fixed-confidence guarantees for Bayesian best-arm identification AISTATS 2020 Foolproof Cooperative Learning ACML 2020 Formal Limitations on the Measurement of Mutual Information AISTATS 2020 Frequency Bias in Neural Networks for Input of Non-Uniform Density ICML 2020 Frequentist Regret Bounds for Randomized Least-Squares Value Iteration AISTATS 2020 From Boltzmann Machines to Neural Networks and Back Again NeurIPS 2020 From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model ICML 2020 Gamification of Pure Exploration for Linear Bandits ICML 2020 Generalization and Representational Limits of Graph Neural Networks ICML 2020 Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics NeurIPS 2020 Generalization Bound of Gradient Descent for Non-Convex Metric Learning NeurIPS 2020 Generalization bounds for deep convolutional neural networks ICLR 2020 Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks AAAI 2020 Generalization Guarantees for Imitation Learning CoRL 2020 Generalized Leverage Score Sampling for Neural Networks NeurIPS 2020 Geometric Insights into the Convergence of Nonlinear TD Learning ICLR 2020