61 citations · 285 across the 30 of their papers we have counts for
7 papers · 1 filter
A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi +1
Gaussian process (GP) modeling is widely used in computational science and engineering. However, fitting a GP to high-dimensional inputs remains challenging due to the curse of dim…
Bayesian data-driven discovery of partial differential equations with variable coefficients
Aoxue Chen, Yifan Du, Liyao Mars Gao +1
The discovery of Partial Differential Equations (PDEs) is an essential task for applied science and engineering. However, data-driven discovery of PDEs is generally challenging, pr…
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Wei Deng, Guang Lin, Faming Liang
We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm (SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian learning in…
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Wei Deng, Qi Feng, Georgios Karagiannis +2
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction f…
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Wei Deng, Qi Feng, Liyao Gao +2
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC…
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
Wei Deng, Xiao Zhang, Faming Liang +1
We propose a novel adaptive empirical Bayesian method for sparse deep learning, where the sparsity is ensured via a class of self-adaptive spike-and-slab priors. The proposed metho…
SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations
Sheng Zhang, Guang Lin
Data-driven discovery of differential equations has been an emerging research topic. We propose a novel algorithm subsampling-based threshold sparse Bayesian regression (SubTSBR) t…