3 citations · 4 across the 8 of their papers we have counts for
8 papers
Subsampled Pseudo-posteriors for Scalable Bayesian Moment-condition Inference
Wenshuo Zhao, Rong Tang
Bayesian pseudo-posteriors based on moment conditions, such as Bayesian empirical likelihood (EL) and Bayesian exponentially tilted empirical likelihood (ETEL), provide a robust ro…
Minimax Optimal Rates for Regression on Manifolds and Distributions
Rong Tang, Yun Yang
Distribution regression seeks to estimate the conditional distribution of a multivariate response given a continuous covariate. This approach offers a more complete characterizatio…
Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation
Rong Tang, Lizhen Lin, Yun Yang
We consider a class of conditional forward-backward diffusion models for conditional generative modeling, that is, generating new data given a covariate (or control variable). To f…
Robust Bayesian Inference on Riemannian Submanifold
Rong Tang, Anirban Bhattacharya, Debdeep Pati +1
Manifold-valued parameters routinely arise in modern statistical applications such as in medical imaging, robotics, and computer vision, to name a few. While traditional Bayesian a…
Estimating Distributions with Low-dimensional Structures Using Mixtures of Generative Models
Rong Tang, Yun Yang
There has been a growing interest in statistical inference from data satisfying the so-called manifold hypothesis, assuming data points in the high-dimensional ambient space to lie…
On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling
Rong Tang, Yun Yang
In this paper, we examine the computational complexity of sampling from a Bayesian posterior (or pseudo-posterior) using the Metropolis-adjusted Langevin algorithm (MALA). MALA fir…
Minimax Rate of Distribution Estimation on Unknown Submanifold under Adversarial Losses
Rong Tang, Yun Yang
Statistical inference from high-dimensional data with low-dimensional structures has recently attracted lots of attention. In machine learning, deep generative modeling approaches…
Statistical Inference for Bayesian Risk Minimization via Exponentially Tilted Empirical Likelihood
Rong Tang, Yun Yang
The celebrated Bernstein von-Mises theorem ensures that credible regions from Bayesian posterior are well-calibrated when the model is correctly-specified, in the frequentist sense…