Learning Theory

Consistency of Multiple Kernel Clustering ICML 2023 Constant Approximation for Individual Preference Stable Clustering NeurIPS 2023 Constant regret for sequence prediction with limited advice ALT 2023 Constraint Optimization over Semirings AAAI 2023 Context-lumpable stochastic bandits NeurIPS 2023 Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms COLT 2023 Contextual Conservative Interleaving Bandits ICML 2023 Contextual Reliability: When Different Features Matter in Different Contexts ICML 2023 Contextual Stochastic Block Model: Sharp Thresholds and Contiguity JMLR 2023 Convergence of Actor-Critic with Multi-Layer Neural Networks NeurIPS 2023 Convergence of Adam Under Relaxed Assumptions NeurIPS 2023 Convolutional Neural Operators for robust and accurate learning of PDEs NeurIPS 2023 Corrupting Neuron Explanations of Deep Visual Features ICCV 2023 Counterfactual Evaluation of Peer-Review Assignment Policies NeurIPS 2023 Counterfactual Memorization in Neural Language Models NeurIPS 2023 Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications NeurIPS 2023 Curriculum Learning With Infant Egocentric Videos NeurIPS 2023 Data Similarity is Not Enough to Explain Language Model Performance EMNLP 2023 Data Structures for Density Estimation ICML 2023 Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? ICLR 2023 Deep learning with kernels through RKHM and the Perron-Frobenius operator NeurIPS 2023 Deep linear networks can benignly overfit when shallow ones do JMLR 2023 Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model NeurIPS 2023 Delayed Bandits: When Do Intermediate Observations Help? ICML 2023 Delving into the Openness of CLIP ACL 2023