Learning Theory

Learning explanations that are hard to vary ICLR 2021 Learning Faithful Representations of Causal Graphs ACL 2021 Learning Implicitly with Noisy Data in Linear Arithmetic IJCAI 2021 Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2021 Learning in Multi-Player Stochastic Games UAI 2021 Learning in Non-Cooperative Configurable Markov Decision Processes NeurIPS 2021 Learning Interaction Kernels for Agent Systems on Riemannian Manifolds ICML 2021 Learning Maximum Margin Markov Networks from examples with missing labels ACML 2021 Learning of Structurally Unambiguous Probabilistic Grammars AAAI 2021 Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples L4DC 2021 Learning sparse mixtures of permutations from noisy information COLT 2021 Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks NeurIPS 2021 Learning-to-learn non-convex piecewise-Lipschitz functions NeurIPS 2021 Learning to Learn to be Right for the Right Reasons NAACL 2021 Learning Whenever Learning is Possible: Universal Learning under General Stochastic Processes JMLR 2021 Learning with Gradient Descent and Weakly Convex Losses AISTATS 2021 Learning Within an Instance for Designing High-Revenue Combinatorial Auctions IJCAI 2021 Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs AAAI 2021 Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization AISTATS 2021 Lenient Regret for Multi-Armed Bandits AAAI 2021 Let’s Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework ICML 2021 Leveraging Good Representations in Linear Contextual Bandits ICML 2021 Leveraging Non-uniformity in First-order Non-convex Optimization ICML 2021 Lifelong Learning with Branching Experts ACML 2021 Lifted reasoning meets weighted model integration UAI 2021