4 papers
Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation
Runze Zhao, Yue Yu, Ruhan Wang +2
Continuous-time reinforcement learning (CTRL) provides a natural framework for sequential decision-making in dynamic environments where interactions evolve continuously over time.…
Intrinsic Random Functions and Parametric Covariance Models of Spatio-Temporal Random Processes on the Sphere
Jongwook Kim, Chunfeng Huang, Nicholas Bussberg
Identifying an appropriate covariance function is one of the primary interests in spatial and spatio-temporal statistics because it allows researchers to analyze the dependence str…
Issues with Neural Tangent Kernel Approach to Neural Networks
Haoran Liu, Anthony Tai, David J. Crandall +1
Neural tangent kernels (NTKs) have been proposed to study the behavior of trained neural networks from the perspective of Gaussian processes. An important result in this body of wo…
Guiding Neural Network Initialization via Marginal Likelihood Maximization
Anthony S. Tai, Chunfeng Huang
We propose a simple, data-driven approach to help guide hyperparameter selection for neural network initialization. We leverage the relationship between neural network and Gaussian…