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07/04/2022
Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
We consider the problem of learning the structure of a causal directed a...
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06/23/2021
The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
Many scientific problems require identifying a small set of covariates t...
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02/16/2021
The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
Many real-world decision-making tasks require learning casual relationsh...
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04/27/2020
Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation
Gaussian latent variable models are a key class of Bayesian hierarchical...
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04/27/2020
Hamiltonian Monte Carlo using an embedded Laplace approximation
Latent Gaussian models are a popular class of hierarchical models with a...
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09/10/2019
Covariance Matrix Estimation under Total Positivity for Portfolio Selection
Selecting the optimal Markowitz porfolio depends on estimating the covar...
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05/17/2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Due to the ease of modern data collection, applied statisticians often h...
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05/16/2019
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Discovering interaction effects on a response of interest is a fundament...
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02/27/2019
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Determining the causal structure of a set of variables is critical for b...
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10/09/2018
Data-dependent compression of random features for large-scale kernel approximation
Kernel methods offer the flexibility to learn complex relationships in m...
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03/15/2018