9 papers
ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning
Yilie Huang, Wenpin Tang, Xun Yu Zhou
We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid. Uniform and hand-crafted schedules are stand…
A Zeroth-Order Deep Learning Method for Fully Nonlinear Parabolic Partial Differential Equations with Unknown Coefficients
Yanwei Jia, Du Ouyang, Huyên Pham +1
High-dimensional partial differential equations (PDEs) with unknown coefficients arise widely in scientific machine learning, including continuous-time reinforcement learning, yet…
Amortized Guidance for Image Inpainting with Pretrained Diffusion Models
Yilie Huang, Xun Yu Zhou
We study image inpainting with generative diffusion models. Existing methods typically either train dedicated task-specific models, or adapt a pretrained diffusion model separately…
ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
Yilie Huang, Wenpin Tang, Xunyu Zhou
We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Uniform and hand-crafted grids can be sub…
Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study
Yilie Huang, Yanwei Jia, Xun Yu Zhou
We study continuous-time mean--variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes, ye…
Merton's Problem with Recursive Perturbed Utility
Min Dai, Yuchao Dong, Yanwei Jia +1
The classical Merton investment problem predicts deterministic, state-dependent portfolio rules; however, laboratory and field evidence suggests that individuals often prefer rando…