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08/15/2023
Misspecified Bernstein-Von Mises theorem for hierarchical models
We derive a Bernstein von-Mises theorem in the context of misspecified, ...
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01/08/2023
Skewed Bernstein-von Mises theorem and skew-modal approximations
Deterministic Gaussian approximations of intractable posterior distribut...
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12/21/2022
Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression
We investigate the frequentist properties of the variational sparse Gaus...
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05/06/2022
Optimal recovery and uncertainty quantification for distributed Gaussian process regression
Gaussian Processes (GP) are widely used for probabilistic modeling and i...
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04/27/2022
Uncertainty Quantification for nonparametric regression using Empirical Bayesian neural networks
We propose a new, two-step empirical Bayes-type of approach for neural n...
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04/21/2022
Distributed Nonparametric Estimation under Communication Constraints
In the era of big data, it is necessary to split extremely large data se...
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02/02/2022
Optimal high-dimensional and nonparametric distributed testing under communication constraints
We derive minimax testing errors in a distributed framework where the da...
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09/22/2021
Contraction rates for sparse variational approximations in Gaussian process regression
We study the theoretical properties of a variational Bayes method in the...
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08/12/2021
Analyzing hierarchical multi-view MRI data with StaPLR: An application to Alzheimer's disease classification
Multi-view data refers to a setting where features are divided into feat...
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12/09/2020
Optimal distributed testing in high-dimensional Gaussian models
In this paper study the problem of signal detection in Gaussian noise in...
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10/30/2020
View selection in multi-view stacking: Choosing the meta-learner
Multi-view stacking is a framework for combining information from differ...
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10/22/2020
Spike and slab variational Bayes for high dimensional logistic regression
Variational Bayes (VB) is a popular scalable alternative to Markov chain...
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03/28/2020
Distributed function estimation: adaptation using minimal communication
We investigate whether in a distributed setting, adaptive estimation of ...
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09/26/2019
Debiased Bayesian inference for average treatment effects
Bayesian approaches have become increasingly popular in causal inference...
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04/15/2019
Variational Bayes for high-dimensional linear regression with sparse priors
We study a mean-field variational Bayes (VB) approximation to Bayesian m...
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04/02/2019
Can we trust Bayesian uncertainty quantification from Gaussian process priors with squared exponential covariance kernel?
We investigate the frequentist coverage properties of credible sets resu...
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11/06/2018
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning
In multi-view learning, features are organized into multiple sets called...
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10/25/2018
Fast Exact Bayesian Inference for Sparse Signals in the Normal Sequence Model
We consider exact algorithms for Bayesian inference with model selection...
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08/23/2018
Spike and slab empirical Bayes sparse credible sets
In the sparse normal means model, coverage of adaptive Bayesian posterio...
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04/03/2018
Adaptive distributed methods under communication constraints
We study distributed estimation methods under communication constraints ...
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11/08/2017