3 citations · 4 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…