430 citations · 985 across the 29 of their papers we have counts for
12 papers · 1 filter
Reverse Diffusion Monte Carlo
Xunpeng Huang, Hanze Dong, Yifan Hao +2
We propose a Monte Carlo sampler from the reverse diffusion process. Unlike the practice of diffusion models, where the intermediary updates -- the score functions -- are learned w…
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
Kush Bhatia, Nikki Lijing Kuang, Yi-An Ma +1
Variational inference has recently emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) in large-scale Bayesian inference. The core idea is to trade st…
On Convergence of Federated Averaging Langevin Dynamics
Wei Deng, Qian Zhang, Yi-An Ma +2
We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty quantification and mean predictions with distributed clients. In particular, we generalize beyond normal…
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence
Ghassen Jerfel, Serena Wang, Clara Fannjiang +3
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference. Its main workhorse is optimization over a reverse Kullback-Leibler diver…
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
Wenlong Mou, Yi-An Ma, Martin J. Wainwright +2
We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-order Langevin dynamics for sampling from distributions with log-concave and smooth densities. The higher-orde…
Bayesian Robustness: A Nonasymptotic Viewpoint
Kush Bhatia, Yi-An Ma, Anca D. Dragan +2
We study the problem of robustly estimating the posterior distribution for the setting where observed data can be contaminated with potentially adversarial outliers. We propose Rob…
Is There an Analog of Nesterov Acceleration for MCMC?
Yi-An Ma, Niladri Chatterji, Xiang Cheng +3
We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as optimization on the space of probability measures, with Kullback-Leibler (KL) divergence as the objective fu…
Sampling Can Be Faster Than Optimization
Yi-An Ma, Yuansi Chen, Chi Jin +2
Optimization algorithms and Monte Carlo sampling algorithms have provided the computational foundations for the rapid growth in applications of statistical machine learning in rece…
Stochastic Gradient MCMC for State Space Models
Christopher Aicher, Yi-An Ma, Nicholas J. Foti +1
State space models (SSMs) are a flexible approach to modeling complex time series. However, inference in SSMs is often computationally prohibitive for long time series. Stochastic…
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
Niladri S. Chatterji, Nicolas Flammarion, Yi-An Ma +2
We provide convergence guarantees in Wasserstein distance for a variety of variance-reduction methods: SAGA Langevin diffusion, SVRG Langevin diffusion and control-variate underdam…
Estimate exponential memory decay in Hidden Markov Model and its applications
Felix X. -F. Ye, Yi-an Ma, Hong Qian
Inference in hidden Markov model has been challenging in terms of scalability due to dependencies in the observation data. In this paper, we utilize the inherent memory decay in hi…
Stochastic Gradient MCMC Methods for Hidden Markov Models
Yi-An Ma, Nicholas J. Foti, Emily B. Fox
Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scaling Bayesian inference to large datasets under an assumption of i.i.d data. We instead develop an SG-MCMC al…