17 citations · 20 across the 16 of their papers we have counts for
16 papers
Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian
Wenpin Tang, Nizar Touzi, Zikun Zhang +1
Diffusion models have achieved remarkable success in generating samples from unknown data distributions. Most popular stochastic differential equation-based diffusion models pertur…
Image AID via continuous-time reinforcement learning
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…
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…
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…
Data-Driven Exploration for a Class of Continuous-Time Indefinite Linear--Quadratic Reinforcement Learning Problems
Yilie Huang, Xun Yu Zhou
We study reinforcement learning (RL) for the same class of continuous-time stochastic linear--quadratic (LQ) control problems as in \cite{huang2024sublinear}, where volatilities de…
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…
Regret of exploratory policy improvement and -learning
Wenpin Tang, Xun Yu Zhou
We study the convergence of -learning and related algorithms introduced by Jia and Zhou (J. Mach. Learn. Res., 24 (2023), 161) for controlled diffusion processes. For explorator…
Reinforcement Learning for Jump-Diffusions, with Financial Applications
Xuefeng Gao, Lingfei Li, Xun Yu Zhou
We study continuous-time reinforcement learning (RL) for stochastic control in which system dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized e…
Data-Driven Merton's Strategies via Policy Randomization
Min Dai, Yuchao Dong, Yanwei Jia +1
We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primi…
Variable Clustering via Distributionally Robust Nodewise Regression
Kaizheng Wang, Xiao Xu, Xun Yu Zhou
We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust version of nodewise regression. To s…
Square-root regret bounds for continuous-time episodic Markov decision processes
Xuefeng Gao, Xun Yu Zhou
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-horizon episodic setting. In contrast to discrete-time MDPs, the inter-transition…
Choquet regularization for reinforcement learning
Xia Han, Ruodu Wang, Xun Yu Zhou
We propose \emph{Choquet regularizers} to measure and manage the level of exploration for reinforcement learning (RL), and reformulate the continuous-time entropy-regularized RL pr…
q-Learning in Continuous Time
Yanwei Jia, Xun Yu Zhou
We study the continuous-time counterpart of Q-learning for reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation introduced by Wang e…
Logarithmic regret bounds for continuous-time average-reward Markov decision processes
Xuefeng Gao, Xun Yu Zhou
We consider reinforcement learning for continuous-time Markov decision processes (MDPs) in the infinite-horizon, average-reward setting. In contrast to discrete-time MDPs, a contin…
Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms
Yanwei Jia, Xun Yu Zhou
We study policy gradient (PG) for reinforcement learning in continuous time and space under the regularized exploratory formulation developed by Wang et al. (2020). We represent th…
Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach
Yanwei Jia, Xun Yu Zhou
We propose a unified framework to study policy evaluation (PE) and the associated temporal difference (TD) methods for reinforcement learning in continuous time and space. We show…