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stat.ML2026

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…

stat.ML2026

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…

stat.ML2026

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…

stat.ML2026

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…

stat.ML2026

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…