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04/06/2023
Spectral Gap Regularization of Neural Networks
We introduce Fiedler regularization, a novel approach for regularizing n...
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02/01/2023
Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery
In many areas of science and engineering, computer simulations are widel...
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11/27/2022
Detecting Changes in the Transmission Rate of a Stochastic Epidemic Model
Throughout the course of an epidemic, the rate at which disease spreads ...
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10/17/2022
Inferring Synergistic and Antagonistic Interactions in Mixtures of Exposures
There is abundant interest in assessing the joint effects of multiple ex...
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10/10/2022
Interpretable AI for relating brain structural and functional connectomes
One of the central problems in neuroscience is understanding how brain s...
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10/08/2022
Spatial predictions on physically constrained domains: Applications to Arctic sea salinity data
In this paper, we predict sea surface salinity (SSS) in the Arctic Ocean...
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01/28/2022
Multiscale Graph Comparison via the Embedded Laplacian Distance
We introduce a simple and fast method for comparing graphs of different ...
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12/13/2021
Limits of epidemic prediction using SIR models
The Susceptible-Infectious-Recovered (SIR) equations and their extension...
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11/22/2021
Using prior information to boost power in correlation structure support recovery
Hypothesis testing of structure in correlation and covariance matrices i...
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07/29/2021
Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching
A wide class of Bayesian models involve unidentifiable random matrices t...
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07/09/2021
Gaussian Process Subspace Regression for Model Reduction
Subspace-valued functions arise in a wide range of problems, including p...
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06/21/2021
Scalable Bayesian inference for time series via divide-and-conquer
Bayesian computational algorithms tend to scale poorly as data size incr...
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11/12/2020
Bayesian nonparametric modelling of sequential discoveries
We aim at modelling the appearance of distinct tags in a sequence of lab...
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10/23/2020
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
Transformation-based methods have been an attractive approach in non-par...
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10/05/2020
Multi-scale graph principal component analysis for connectomics
In brain connectomics, the cortical surface is parcellated into differen...
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08/18/2020
Bayesian neural networks and dimensionality reduction
In conducting non-linear dimensionality reduction and feature learning, ...
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08/17/2020
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction clustering
Even with the rise in popularity of over-parameterized models, simple di...
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04/10/2020
Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity
Factor models are routinely used for dimensionality reduction in modelin...
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04/08/2020
Posterior computation with the Gibbs zig-zag sampler
Markov chain Monte Carlo (MCMC) sampling algorithms have dominated the l...
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03/02/2020
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
We introduce a novel regularization approach for deep learning that inco...
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12/27/2019
Bayesian joint modeling of chemical structure and dose response curves
Today there are approximately 85,000 chemicals regulated under the Toxic...
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10/06/2019
Predicting Phenotypes from Brain Connection Structure
This article focuses on the problem of predicting a response variable ba...
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08/20/2019
Bayesian Hierarchical Factor Regression Models to Infer Cause of Death From Verbal Autopsy Data
In low-resource settings where vital registration of death is not routin...
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05/27/2019
Efficient posterior sampling for high-dimensional imbalanced logistic regression
High-dimensional data are routinely collected in many application areas....
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04/24/2019
Stochastic Lipschitz Q-Learning
In an episodic Markov Decision Process (MDP) problem, an online algorith...
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09/10/2018
Maximum Pairwise Bayes Factors for Covariance Structure Testing
Hypothesis testing of structure in covariance matrices is of significant...
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06/07/2018
Tensor network factorizations: Relationships between brain structural connectomes and traits
Advanced brain imaging techniques make it possible to measure individual...
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05/21/2018
Non-Oscillatory Pattern Learning for Non-Stationary Signals
This paper proposes a novel non-oscillatory pattern (NOP) learning schem...
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04/25/2018
Symmetric Bilinear Regression for Signal Subgraph Estimation
There is increasing interest in learning a set of small outcome-relevant...
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04/01/2018
Bayesian Mosaic: Parallelizable Composite Posterior
This paper proposes Bayesian mosaic, a parallelizable composite posterio...
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02/15/2018
Reducing over-clustering via the powered Chinese restaurant process
Dirichlet process mixture (DPM) models tend to produce many small cluste...
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01/03/2018
Intrinsic Gaussian processes on complex constrained domains
We propose a class of intrinsic Gaussian processes (in-GPs) for interpol...
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07/20/2017
Common and Individual Structure of Multiple Networks
This article focuses on the problem of studying shared- and individual-s...
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02/08/2016
DECOrrelated feature space partitioning for distributed sparse regression
Fitting statistical models is computationally challenging when the sampl...
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06/07/2015
No penalty no tears: Least squares in high-dimensional linear models
Ordinary least squares (OLS) is the default method for fitting linear mo...
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10/24/2014
Median Selection Subset Aggregation for Parallel Inference
For massive data sets, efficient computation commonly relies on distribu...
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04/22/2013
Bayesian crack detection in ultra high resolution multimodal images of paintings
The preservation of our cultural heritage is of paramount importance. Th...
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12/15/2011
Beta-Negative Binomial Process and Poisson Factor Analysis
A beta-negative binomial (BNB) process is proposed, leading to a beta-ga...
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04/05/2011
Generalized double Pareto shrinkage
We propose a generalized double Pareto prior for Bayesian shrinkage esti...
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01/11/2011
