
Accelerating Stochastic Simulation with Interactive Neural Processes
Stochastic simulations such as largescale, spatiotemporal, agestructur...
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Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Deep learning is gaining increasing popularity for spatiotemporal foreca...
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Efficient and Scalable Bayesian Neural Nets with Rank1 Factors
Bayesian neural networks (BNNs) demonstrate promising success in improvi...
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On Thompson Sampling with Langevin Algorithms
Thompson sampling is a methodology for multiarmed bandit problems that ...
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HighOrder Langevin Diffusion Yields an Accelerated MCMC Algorithm
We propose a Markov chain Monte Carlo (MCMC) algorithm based on thirdor...
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Bayesian Robustness: A Nonasymptotic Viewpoint
We study the problem of robustly estimating the posterior distribution f...
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Is There an Analog of Nesterov Acceleration for MCMC?
We formulate gradientbased Markov chain Monte Carlo (MCMC) sampling as ...
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Sampling Can Be Faster Than Optimization
Optimization algorithms and Monte Carlo sampling algorithms have provide...
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Stochastic Gradient MCMC for State Space Models
State space models (SSMs) are a flexible approach to modeling complex ti...
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Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cos...
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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 ...
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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...
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Stochastic Gradient MCMC Methods for Hidden Markov Models
Stochastic gradient MCMC (SGMCMC) algorithms have proven useful in scal...
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A Complete Recipe for Stochastic Gradient MCMC
Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous...
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Yian Ma
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