Learning Theory

Learnability of Competitive Threshold Models IJCAI 2022 Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness ICLR 2022 Learning and Covering Sums of Independent Random Variables with Unbounded Support NeurIPS 2022 Learning Continuous Graph Structure with Bilevel Programming for Graph Neural Networks IJCAI 2022 Learning Curves for SGD on Structured Features ICLR 2022 Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers NeurIPS 2022 Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales NeurIPS 2022 Learning from Label Proportions by Learning with Label Noise NeurIPS 2022 LEARNING GUARANTEES FOR GRAPH CONVOLUTIONAL NETWORKS ON THE STOCHASTIC BLOCK MODEL ICLR 2022 Learning Higher-Order Logic Programs From Failures IJCAI 2022 Learning Influence Adoption in Heterogeneous Networks AAAI 2022 Learning invariant weights in neural networks UAI 2022 Learning Losses for Strategic Classification AAAI 2022 Learning Low Degree Hypergraphs COLT 2022 Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits ICML 2022 Learning Multi-Objective Curricula for Robotic Policy Learning CoRL 2022 Learning on Arbitrary Graph Topologies via Predictive Coding NeurIPS 2022 Learning Partial Equivariances From Data NeurIPS 2022 Learning POMDP Models with Similarity Space Regularization: a Linear Gaussian Case Study L4DC 2022 Learning Predictions for Algorithms with Predictions NeurIPS 2022 Learning single-index models with shallow neural networks NeurIPS 2022 Learning sparse features can lead to overfitting in neural networks NeurIPS 2022 Learning Stochastic Shortest Path with Linear Function Approximation ICML 2022 Learning to Control Linear Systems can be Hard COLT 2022 Learning to Plan Variable Length Sequences of Actions with a Cascading Bandit Click Model of User Feedback AISTATS 2022