Learning Theory

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent ICML 2020 Synthetic Data Generators -- Sequential and Private NeurIPS 2020 Taking a hint: How to leverage loss predictors in contextual bandits? COLT 2020 Teaching with Limited Information on the Learner’s Behaviour ICML 2020 Temporal Variability in Implicit Online Learning NeurIPS 2020 Tensorized Random Projections AISTATS 2020 The All-or-Nothing Phenomenon in Sparse Tensor PCA NeurIPS 2020 The asymptotic spectrum of the Hessian of DNN throughout training ICLR 2020 The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise NeurIPS 2020 The estimation error of general first order methods COLT 2020 The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent ICML 2020 The Implications of Local Correlation on Learning Some Deep Functions NeurIPS 2020 The intriguing role of module criticality in the generalization of deep networks ICLR 2020 The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning NeurIPS 2020 The Many Shapley Values for Model Explanation ICML 2020 The Mean-Squared Error of Double Q-Learning NeurIPS 2020 The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization ICML 2020 Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View NeurIPS 2020 Theory of Curriculum Learning, with Convex Loss Functions JMLR 2020 The Pitfalls of Simplicity Bias in Neural Networks NeurIPS 2020 The Power of Comparisons for Actively Learning Linear Classifiers NeurIPS 2020 The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture ICML 2020 The Sample Complexity of Best-k Items Selection from Pairwise Comparisons ICML 2020 The Statistical Cost of Robust Kernel Hyperparameter Turning NeurIPS 2020 The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks NeurIPS 2020