Yi-an Ma

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berkeley college ∙ University of California, San Diego

Accelerating Stochastic Simulation with Interactive Neural Processes

Stochastic simulations such as large-scale, spatiotemporal, age-structur...
06/05/2021 ∙ by Dongxia Wu, et al. ∙ 0 ∙ share

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Quantifying Uncertainty in Deep Spatiotemporal Forecasting

Deep learning is gaining increasing popularity for spatiotemporal foreca...
05/25/2021 ∙ by Dongxia Wu, et al. ∙ 0 ∙ 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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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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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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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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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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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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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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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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Yi-an Ma

Featured Co-authors

Xin Wang

Michael I. Jordan

Trevor Darrell

Martin J. Wainwright

Joseph E. Gonzalez

Peter L. Bartlett

Anca D. Dragan

Chi Jin

Balaji Lakshminarayanan

Dustin Tran

Aldo Pacchiano

Accelerating Stochastic Simulation with Interactive Neural Processes

Quantifying Uncertainty in Deep Spatiotemporal Forecasting

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors

On Thompson Sampling with Langevin Algorithms

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

Bayesian Robustness: A Nonasymptotic Viewpoint

Is There an Analog of Nesterov Acceleration for MCMC?

Sampling Can Be Faster Than Optimization

Stochastic Gradient MCMC for State Space Models

Deep Mixture of Experts via Shallow Embedding

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo

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