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The Role of Contextual Information in Best Arm Identification
We study the best-arm identification problem with fixed confidence when contextual (covariate) information is available in stochastic bandits. In each round, we observe contextual information before selecting an arm. The distribution of the reward associated with the selected arm depends on the observed contextual information. We are interested in finding the arm with the maximu...

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A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks
To understand the training dynamics of neural networks, prior studies have considered the mean-field (MF) limit of two-layer NNs as the width tends to infinity, establishing theoretical guarantees for its convergence under gradient flow training as well as approximation and generalization capabilities. In this work, we study the infinite-width limit of a type of three-layer neur...

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Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
Penalized empirical risk minimization with a surrogate loss function is often used to learn a high-dimensional linear decision rule in classification problems. Although much of the literature focus on the generalization error, there is a lack of inference procedures for identifying the driving factors of the estimated decision rule, especially when the surrogate loss is non-diff...

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Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation
As machine learning models and datasets continue to grow, developing complex models has become increasingly computationally demanding. Knowledge distillation reduces deployment cost by compressing a large, well-trained teacher model into a compact student model, but it does not address settings where constructing the teacher itself is the bottleneck. Motivated by this challenge,...

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Neural Exploitation and Exploration of Contextual Bandits
In this paper, we study the neural exploration strategy for contextual bandits. The dilemma of exploitation and exploration widely exists in real-world applications such as recommender systems, online advertising, and clinical trials. Contextual bandits provide principled methods to solve this dilemma, including two prevalent techniques: Thompson Sampling (TS), and Upper Confide...

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