8 papers · 1 filter
Conditional Flow Models for Offline Maximum Entropy Reinforcement Learning
Hao Cheng, Yuling Jiao, Lican Kang +2
Offline maximum entropy reinforcement learning seeks to estimate the optimal soft action-value function \(Q^*\) from a fixed dataset without further interaction with the environmen…
Schrödinger--Föllmer Actor--Critic: Diffusion Policy Improvement with Finite-Sample Analysis
Yuling Jiao, Lican Kang, Jerry Zhijian Yang +1
Diffusion policies represent multimodal action distributions, but an advantage-weighted update does not specify how to sample from the resulting target distribution. We propose Sch…
Offline Deep Q* Estimation with Diffusion Models
Xiaohong Chen, Yuling Jiao, Lican Kang +2
In offline RL, estimating the optimal action-value function can be formulated as solving the optimal Bellman equation based solely on offline observations. A fundamental chal…
Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression
Xingdong Feng, Xinhong Jiang, Yuling Jiao +2
This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed co…
Distributional Off-Policy Evaluation with Deep Quantile Process Regression
Qi Kuang, Chao Wang, Yuling Jiao +1
This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most ex…
Deep Bootstrap
Jinyuan Chang, Yuling Jiao, Lican Kang +1
In this work, we propose a novel deep bootstrap framework for nonparametric regression based on conditional diffusion models. Specifically, we construct a conditional diffusion mod…
Model Free Prediction with Uncertainty Assessment
Yuling Jiao, Lican Kang, Jin Liu +2
Deep nonparametric regression, characterized by the utilization of deep neural networks to learn target functions, has emerged as a focus of research attention in recent years. Des…
Latent Schr{ö}dinger Bridge Diffusion Model for Generative Learning
Yuling Jiao, Lican Kang, Huazhen Lin +2
This paper aims to conduct a comprehensive theoretical analysis of current diffusion models. We introduce a novel generative learning methodology utilizing the Schr{ö}dinger bridge…