Learning Theory

Consistent Estimators for Learning to Defer to an Expert ICML 2020 Consistent MetaReg: Alleviating Intra-task Discrepancy for Better Meta-knowledge IJCAI 2020 Consistent Structured Prediction with Max-Min Margin Markov Networks ICML 2020 Constrained Upper Confidence Reinforcement Learning L4DC 2020 Cooperative Multi-Agent Bandits with Heavy Tails ICML 2020 Cost-Accuracy Aware Adaptive Labeling for Active Learning AAAI 2020 Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models ICML 2020 Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness ICML 2020 Dataless Model Selection With the Deep Frame Potential CVPR 2020 Decision trees as partitioning machines to characterize their generalization properties NeurIPS 2020 Deep Learning for Abstract Argumentation Semantics IJCAI 2020 Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel NeurIPS 2020 Differentiable Grammars for Videos AAAI 2020 Directional convergence and alignment in deep learning NeurIPS 2020 Disentangling Trainability and Generalization in Deep Neural Networks ICML 2020 Distributed Kernel Ridge Regression with Communications JMLR 2020 Distribution Free Learning with Local Queries ALT 2020 DNNs as Layers of Cooperating Classifiers AAAI 2020 Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making ICML 2020 Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution EMNLP 2020 Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? ACL 2020 Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime ICML 2020 Dual Learning: Theoretical Study and an Algorithmic Extension ACML 2020 Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification NeurIPS 2020 Dynamics of Deep Neural Networks and Neural Tangent Hierarchy ICML 2020