Rong Tang, Lizhen Lin and Yun Yang (2025). Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation.  International Conference on Learning Representations (ICLR), 2025

Rong Tang and Yun Yang (2024). On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling. Journal of Machine Learning Research [arXiv].

Rong Tang and Yun Yang (2024). Adaptivity of Diffusion Models to Manifold Structures. 27th International Conference on Artificial Intelligence and Statistics (AISTATS)  [proceeding].

Rong Tang and Yun Yang (2023). Minimax Rate of Distribution Estimation on Unknown Submanifold under Adversarial Losses. Annals of Statistics [journal].

Rong Tang and Yun Yang (2023). Minimax Nonparametric Two-Sample Test under Adversarial Losses. 26th International Conference on Artificial Intelligence and Statistics (AISTATS) [proceeding].

Rong Tang and Yun Yang (2022). Bayesian Inference for Risk Minimization via Exponentially Tilted Empirical Likelihood. Journal of the Royal Statistical Society: Series B [journal]. 

Rong Tang and Yun Yang (2021). On Empirical Bayes Variational Autoencoder: An Excess Risk Bound. Proceedings of Thirty-Fourth Conference on Learning Theory (COLT 2021) [proceeding].  

Rong Tang and Yun Yang. Minimax Optimal Rates for Regression on Manifolds and Distributions [arXiv] 

Rong Tang, Anirban Bhattacharya, Debdeep Pati and Yun Yang.  Robust Bayesian Inference on Riemannian Submanifold [arXiv] 

Rong Tang and Yun Yang. Estimating Distributions with Low-dimensional Structures Using Mixtures of Generative Models [arXiv].