Yi-an Ma

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berkeley college

Bayesian Robustness: A Nonasymptotic Viewpoint

We study the problem of robustly estimating the posterior distribution f...
07/27/2019 ∙ by Kush Bhatia, et al. ∙ 18 ∙ share

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High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

We propose a Markov chain Monte Carlo (MCMC) algorithm based on third-or...
08/28/2019 ∙ by Wenlong Mou, et al. ∙ 12 ∙ share

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Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

Bayesian neural networks (BNNs) demonstrate promising success in improvi...
05/14/2020 ∙ by Michael W. Dusenberry, et al. ∙ 11 ∙ share

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On Thompson Sampling with Langevin Algorithms

Thompson sampling is a methodology for multi-armed bandit problems that ...
02/23/2020 ∙ by Eric Mazumdar, et al. ∙ 9 ∙ share

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Is There an Analog of Nesterov Acceleration for MCMC?

We formulate gradient-based Markov chain Monte Carlo (MCMC) sampling as ...
02/04/2019 ∙ by Yi-an Ma, et al. ∙ 4 ∙ share

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Estimate exponential memory decay in Hidden Markov Model and its applications

Inference in hidden Markov model has been challenging in terms of scalab...
10/17/2017 ∙ by Felix X. -F. Ye, et al. ∙ 0 ∙ share

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Stochastic Gradient MCMC Methods for Hidden Markov Models

Stochastic gradient MCMC (SG-MCMC) algorithms have proven useful in scal...
06/14/2017 ∙ by Yi-an Ma, et al. ∙ 0 ∙ share

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A Complete Recipe for Stochastic Gradient MCMC

Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous...
06/15/2015 ∙ by Yi-an Ma, et al. ∙ 0 ∙ share

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On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

We provide convergence guarantees in Wasserstein distance for a variety ...
02/15/2018 ∙ by Niladri S. Chatterji, et al. ∙ 0 ∙ share

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Deep Mixture of Experts via Shallow Embedding

Larger networks generally have greater representational power at the cos...
06/05/2018 ∙ by Xin Wang, et al. ∙ 0 ∙ share

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Sampling Can Be Faster Than Optimization

Optimization algorithms and Monte Carlo sampling algorithms have provide...
11/20/2018 ∙ by Yi-an Ma, et al. ∙ 0 ∙ share

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Stochastic Gradient MCMC for State Space Models

State space models (SSMs) are a flexible approach to modeling complex ti...
10/22/2018 ∙ by Christopher Aicher, et al. ∙ 0 ∙ share

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Yi-an Ma

Featured Co-authors

Michael I. Jordan

Xin Wang

Trevor Darrell

Martin J. Wainwright

Peter L. Bartlett

Joseph E. Gonzalez

Anca D. Dragan

Dustin Tran

Emily B. Fox

Chi Jin

Fisher Yu

Bayesian Robustness: A Nonasymptotic Viewpoint

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

On Thompson Sampling with Langevin Algorithms

Is There an Analog of Nesterov Acceleration for MCMC?

Estimate exponential memory decay in Hidden Markov Model and its applications

Stochastic Gradient MCMC Methods for Hidden Markov Models

A Complete Recipe for Stochastic Gradient MCMC

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

Deep Mixture of Experts via Shallow Embedding

Sampling Can Be Faster Than Optimization

Stochastic Gradient MCMC for State Space Models