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12/08/2022
A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data
Graphical models have long been studied in statistics as a tool for infe...
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11/20/2022
Non-reversible Parallel Tempering for Deep Posterior Approximation
Parallel tempering (PT), also known as replica exchange, is the go-to wo...
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10/09/2022
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network
Sufficient dimension reduction is a powerful tool to extract core inform...
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02/20/2022
Interacting Contour Stochastic Gradient Langevin Dynamics
We propose an interacting contour stochastic gradient Langevin dynamics ...
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01/14/2022
A Kernel-Expanded Stochastic Neural Network
The deep neural network suffers from many fundamental issues in machine ...
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10/01/2021
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration
Deep learning has powered recent successes of artificial intelligence (A...
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05/11/2021
A Langevinized Ensemble Kalman Filter for Large-Scale Static and Dynamic Learning
The Ensemble Kalman Filter (EnKF) has achieved great successes in data a...
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02/25/2021
Consistent Sparse Deep Learning: Theory and Computation
Deep learning has been the engine powering many successes of data scienc...
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12/17/2020
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols
Many proximity-based tracing (PCT) protocols have been proposed and depl...
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10/19/2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
We propose an adaptively weighted stochastic gradient Langevin dynamics ...
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10/17/2020
Markov Neighborhood Regression for High-Dimensional Inference
This paper proposes an innovative method for constructing confidence int...
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10/02/2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
Replica exchange stochastic gradient Langevin dynamics (reSGLD) has show...
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09/20/2020
Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts
Bayesian deep learning offers a principled way to address many issues co...
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08/12/2020
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Replica exchange Monte Carlo (reMC), also known as parallel tempering, i...
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02/07/2020
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have rece...
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10/23/2019
An Adaptive Empirical Bayesian Method for Sparse Deep Learning
We propose a novel adaptive empirical Bayesian (AEB) method for sparse d...
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10/11/2018
Stochastic Approximation Hamiltonian Monte Carlo
Recently, the Hamilton Monte Carlo (HMC) has become widespread as one of...
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05/07/2018
Learning Multiple Gene Regulatory Networks in Type 1 Diabetes through a Fast Bayesian Integrative Method
Accurate inference of Gene Regulatory Networks (GRNs) is pivotal to gain...
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05/07/2018
Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data
The Gaussian graphical model is a widely used tool for learning gene reg...
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02/22/2018
A Bayesian Mark Interaction Model for Analysis of Tumor Pathology Images
With the advance of imaging technology, digital pathology imaging of tum...
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02/06/2018
An Imputation-Consistency Algorithm for High-Dimensional Missing Data Problems and Beyond
Missing data are frequently encountered in high-dimensional problems, bu...
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12/24/2017