96 citations · 120 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…