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02/24/2023
A Targeted Accuracy Diagnostic for Variational Approximations
Variational Inference (VI) is an attractive alternative to Markov Chain ...
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07/25/2022
Statistical Inference with Stochastic Gradient Algorithms
Tuning of stochastic gradient algorithms (SGAs) for optimization and sam...
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03/29/2022
Robust, Automated, and Accurate Black-box Variational Inference
Black-box variational inference (BBVI) now sees widespread use in machin...
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03/29/2022
Calibrated Model Criticism Using Split Predictive Checks
Checking how well a fitted model explains the data is one of the most fu...
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09/01/2020
Robust, Accurate Stochastic Optimization for Variational Inference
We consider the problem of fitting variational posterior approximations ...
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07/24/2020
Robust and Reproducible Model Selection Using Bagged Posteriors
Bayesian model selection is premised on the assumption that the data are...
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12/15/2019
Using bagged posteriors for robust inference and model criticism
Standard Bayesian inference is known to be sensitive to model misspecifi...
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10/09/2019
Practical Posterior Error Bounds from Variational Objectives
Variational inference has become an increasingly attractive, computation...
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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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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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09/25/2018
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximatio...
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06/26/2018
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
Gaussian processes (GPs) offer a flexible class of priors for nonparamet...
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06/20/2018
Random Feature Stein Discrepancies
Computable Stein discrepancies have been deployed for a variety of appli...
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09/26/2017
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Generalized linear models (GLMs) -- such as logistic regression, Poisson...
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05/20/2016
Coresets for Scalable Bayesian Logistic Regression
The use of Bayesian methods in large-scale data settings is attractive b...
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05/20/2016
Quantifying the accuracy of approximate diffusions and Markov chains
Markov chains and diffusion processes are indispensable tools in machine...
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05/19/2015
Risk and Regret of Hierarchical Bayesian Learners
Common statistical practice has shown that the full power of Bayesian me...
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03/01/2015
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Markov jump processes (MJPs) are used to model a wide range of phenomena...
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12/31/2014
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models
In this note we provide detailed derivations of two versions of small-va...
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06/30/2014
Infinite Structured Hidden Semi-Markov Models
This paper reviews recent advances in Bayesian nonparametric techniques ...
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07/02/2013