Pan Xu

Preprint · May 24, 2026 We study inference-time alignment for diffusion-based generative models, aiming to steer a base model toward high-reward outputs without updating its weights. Recent Sequential Monte Carlo (SMC)-based steering methods approximate reward-tilted target distr ... Link to item Cite

Cross-Domain Energy-Guided Diffusion Generation for Off-Dynamics Reinforcement Learning

Preprint · May 23, 2026 Off-dynamics offline reinforcement learning seeks to learn a target-domain policy from a large source dataset and a limited target dataset under mismatched transition dynamics. Existing approaches such as reward augmentation and data filtering are constrai ... Link to item Cite

Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning

Preprint · February 24, 2026 Link to item Cite

Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction

Preprint · November 7, 2025 Link to item Cite

Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation

Preprint · October 15, 2025 Link to item Cite

Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits

Preprint · October 12, 2025 Link to item Cite

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective

Preprint · October 6, 2025 Link to item Cite

How to Provably Improve Return Conditioned Supervised Learning?

Preprint · June 10, 2025 Link to item Cite

Linear Mixture Distributionally Robust Markov Decision Processes

Preprint · May 23, 2025 Link to item Cite

Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning

Preprint · October 30, 2024 Link to item Cite

Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning

Preprint · September 30, 2024 Link to item Cite

Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization

Preprint · October 24, 2023 Link to item Cite

PhyGCN: Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning

Preprint · October 2, 2023 Full text Cite

Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits

Preprint · September 15, 2023 Link to item Cite

Challenges of COVID-19 Case Forecasting in the US, 2020-2021

Preprint · June 4, 2023 Full text Cite

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Preprint · 2021 Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Fo ... Full text Cite

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Preprint · November 5, 2020 Full text Cite

Ensemble Forecasts of Coronavirus Disease 2019 (COVID-19) in the U.S.

Preprint · 2020 Background The COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research has suggested that combining forecasts from multiple models into a single “ensemble” forecast can increase the robustness of forecast ... Full text Cite

Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States

Preprint · 2020 Full text Cite

Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models

Preprint · December 29, 2016 Link to item Cite