Theory

On Convergence and Generalization of Dropout Training NeurIPS 2020 On Convergence of Nearest Neighbor Classifiers over Feature Transformations NeurIPS 2020 On Correctness of Automatic Differentiation for Non-Differentiable Functions NeurIPS 2020 On hyperparameter tuning in general clustering problemsm ICML 2020 On Infinite-Width Hypernetworks NeurIPS 2020 On Irrelevant Literals in Pseudo-Boolean Constraint Learning IJCAI 2020 On Learnability wih Computable Learners ALT 2020 On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration COLT 2020 Online Multitask Learning with Long-Term Memory NeurIPS 2020 Online Revenue Maximization for Server Pricing IJCAI 2020 On Performance Estimation in Automatic Algorithm Configuration AAAI 2020 On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis AISTATS 2020 On Reward-Free Reinforcement Learning with Linear Function Approximation NeurIPS 2020 On Robustness in Qualitative Constraint Networks IJCAI 2020 On Robustness of Neural Ordinary Differential Equations ICLR 2020 On Testing of Samplers NeurIPS 2020 On the Computational Power of Transformers and Its Implications in Sequence Modeling CoNLL 2020 On the consistency of top-k surrogate losses ICML 2020 On the Convergence of Model Free Learning in Mean Field Games AAAI 2020 On the Convergence of Smooth Regularized Approximate Value Iteration Schemes NeurIPS 2020 On the Decidability of Intuitionistic Tense Logic without Disjunction IJCAI 2020 On the Discrepancy between Density Estimation and Sequence Generation EMNLP 2020 On the Equivalence between Online and Private Learnability beyond Binary Classification NeurIPS 2020 On the Equivalence between Positional Node Embeddings and Structural Graph Representations ICLR 2020 On the Generalization Benefit of Noise in Stochastic Gradient Descent ICML 2020