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12/01/2022
Are you using test log-likelihood correctly?
Test log-likelihood is commonly used to compare different models of the ...
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09/29/2022
How good is your Gaussian approximation of the posterior? Finite-sample computable error bounds for a variety of useful divergences
The Bayesian Central Limit Theorem (BCLT) for finite-dimensional models,...
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07/14/2022
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U.S. Government ...
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06/08/2022
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Construction of a scaffold structure that supports a desired motif, conf...
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04/04/2022
A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users
Some individuals with motor impairments communicate using a single switc...
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02/23/2022
Many processors, little time: MCMC for partitions via optimal transport couplings
Markov chain Monte Carlo (MCMC) methods are often used in clustering sin...
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12/05/2021
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Probabilistic machine learning increasingly informs critical decisions i...
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12/03/2021
Bayesian nonparametric strategies for power maximization in rare variants association studies
Rare variants are hypothesized to be largely responsible for heritabilit...
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07/19/2021
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
Models like LASSO and ridge regression are extensively used in practice ...
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07/13/2021
For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Hierarchical Bayesian methods enable information sharing across multiple...
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06/29/2021
Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation
There is a growing interest in the estimation of the number of unseen fe...
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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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06/11/2021
Measuring the sensitivity of Gaussian processes to kernel choice
Gaussian processes (GPs) are used to make medical and scientific decisio...
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04/09/2021
Optimal transport couplings of Gibbs samplers on partitions for unbiased estimation
Computational couplings of Markov chains provide a practical route to un...
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02/19/2021
Confidently Comparing Estimators with the c-value
Modern statistics provides an ever-expanding toolkit for estimating unkn...
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11/30/2020
An Automatic Finite-Sample Robustness Metric: Can Dropping a Little Data Change Conclusions?
We propose a method to assess the sensitivity of econometric analyses to...
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09/22/2020
Independent finite approximations for Bayesian nonparametric inference: construction, error bounds, and practical implications
Bayesian nonparametrics based on completely random measures (CRMs) offer...
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08/24/2020
Approximate Cross-Validation with Low-Rank Data in High Dimensions
Many recent advances in machine learning are driven by a challenging tri...
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07/08/2020
Finite mixture models are typically inconsistent for the number of components
Scientists and engineers are often interested in learning the number of ...
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06/23/2020
Approximate Cross-Validation for Structured Models
Many modern data analyses benefit from explicitly modeling dependence st...
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12/11/2019
More for less: Predicting and maximizing genetic variant discovery via Bayesian nonparametrics
While the cost of sequencing genomes has decreased dramatically in recen...
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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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07/28/2019
A Higher-Order Swiss Army Infinitesimal Jackknife
Cross validation (CV) and the bootstrap are ubiquitous model-agnostic to...
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06/22/2019
Local Exchangeability
Exchangeability---in which the distribution of an infinite sequence is i...
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05/31/2019
Sparse Approximate Cross-Validation for High-Dimensional GLMs
Leave-one-out cross validation (LOOCV) can be particularly accurate amon...
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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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11/28/2018
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data
Until recently, transcriptomics was limited to bulk RNA sequencing, obsc...
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10/15/2018
Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics
A central question in many probabilistic clustering problems is how many...
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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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06/01/2018
Return of the Infinitesimal Jackknife
The error or variability of machine learning algorithms is often assesse...
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03/15/2018
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
Learning a Bayesian network (BN) from data can be useful for decision-ma...
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02/05/2018
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Coherent uncertainty quantification is a key strength of Bayesian method...
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12/05/2017
Measuring Cluster Stability for Bayesian Nonparametrics Using the Linear Bootstrap
Clustering procedures typically estimate which data points are clustered...
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10/13/2017
Automated Scalable Bayesian Inference via Hilbert Coresets
The automation of posterior inference in Bayesian data analysis has enab...
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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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11/17/2016
Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian po...
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06/23/2016
Fast robustness quantification with variational Bayes
Bayesian hierarchical models are increasing popular in economics. When u...
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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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03/22/2016
Completely random measures for modeling power laws in sparse graphs
Network data appear in a number of applications, such as online social n...
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03/22/2016
Edge-exchangeable graphs and sparsity
A known failing of many popular random graph models is that the Aldous-H...
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06/12/2015
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Mean field variational Bayes (MFVB) is a popular posterior approximation...
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02/26/2015
Covariance Matrices and Influence Scores for Mean Field Variational Bayes
Mean field variational Bayes (MFVB) is a popular posterior approximation...
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10/24/2014
Covariance Matrices for Mean Field Variational Bayes
Mean Field Variational Bayes (MFVB) is a popular posterior approximation...
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10/17/2014
Variational Bayes for Merging Noisy Databases
Bayesian entity resolution merges together multiple, noisy databases and...
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07/30/2013
Optimistic Concurrency Control for Distributed Unsupervised Learning
Research on distributed machine learning algorithms has focused primaril...
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07/25/2013
Streaming Variational Bayes
We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)syn...
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12/10/2012
