5 papers
Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
Jia-Qi Yang, Lei Shi
We consider a class of statistical inverse problems involving the estimation of a regression operator from a Polish space to a separable Hilbert space, where the target lies in a v…
PlannerRFT: Reinforcing Diffusion Planners through Closed-Loop and Sample-Efficient Fine-Tuning
Hongchen Li, Tianyu Li, Jiazhi Yang +10
Diffusion-based planners have emerged as a promising approach for human-like trajectory generation in autonomous driving. Recent works incorporate reinforcement fine-tuning to enha…
A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
Jia-Qi Yang, Lei Shi
We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer operator-valued kernels. Our framework…
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
Lei Shi, Jia-Qi Yang
This study investigates leveraging stochastic gradient descent (SGD) to learn operators between general Hilbert spaces. We propose weak and strong regularity conditions for the tar…
Zero-Inflated Bandits
Haoyu Wei, Runzhe Wan, Lei Shi +1
Many real-world bandit applications are characterized by sparse rewards, which can significantly hinder learning efficiency. Leveraging problem-specific structures for careful dist…