Tamara Broderick

research

∙ 12/01/2022

Are you using test log-likelihood correctly?

Test log-likelihood is commonly used to compare different models of the ...

0 Sameer K. Deshpande, et al. ∙

research

∙ 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,...

0 Mikołaj J. Kasprzak, et al. ∙

research

∙ 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 ...

5 Vijay Gadepally, et al. ∙

research

∙ 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...

15 Brian L Trippe, et al. ∙

research

∙ 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...

0 Nicholas Bonaker, et al. ∙

research

∙ 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...

0 Tin D. Nguyen, et al. ∙

research

∙ 12/05/2021

Toward a Taxonomy of Trust for Probabilistic Machine Learning

Probabilistic machine learning increasingly informs critical decisions i...

34 Tamara Broderick, et al. ∙

research

∙ 12/03/2021

Bayesian nonparametric strategies for power maximization in rare variants association studies

Rare variants are hypothesized to be largely responsible for heritabilit...

0 Lorenzo Masoero, et al. ∙

research

∙ 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 ...

0 William T. Stephenson, et al. ∙

research

∙ 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...

0 Brian L Trippe, et al. ∙

research

∙ 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...

0 Federico Camerlenghi, et al. ∙

research

∙ 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...

0 Raj Agrawal, et al. ∙

research

∙ 06/11/2021

Measuring the sensitivity of Gaussian processes to kernel choice

Gaussian processes (GPs) are used to make medical and scientific decisio...

0 William T. Stephenson, et al. ∙

research

∙ 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...

0 Brian L Trippe, et al. ∙

research

∙ 02/19/2021

Confidently Comparing Estimators with the c-value

Modern statistics provides an ever-expanding toolkit for estimating unkn...

0 Brian L Trippe, et al. ∙

research

∙ 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...

0 Tamara Broderick, et al. ∙

research

∙ 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...

8 Tin D. Nguyen, et al. ∙

research

∙ 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...

23 William T. Stephenson, et al. ∙

research

∙ 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 ...

7 Diana Cai, et al. ∙

research

∙ 06/23/2020

Approximate Cross-Validation for Structured Models

Many modern data analyses benefit from explicitly modeling dependence st...

0 Soumya Ghosh, et al. ∙

research

∙ 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...

0 Lorenzo Masoero, et al. ∙

research

∙ 10/09/2019

Practical Posterior Error Bounds from Variational Objectives

Variational inference has become an increasingly attractive, computation...

23 Jonathan H. Huggins, et al. ∙

research

∙ 07/28/2019

A Higher-Order Swiss Army Infinitesimal Jackknife

Cross validation (CV) and the bootstrap are ubiquitous model-agnostic to...

3 Ryan Giordano, et al. ∙

research

∙ 06/22/2019

Local Exchangeability

Exchangeability---in which the distribution of an infinite sequence is i...

0 Trevor Campbell, et al. ∙

research

∙ 05/31/2019

Sparse Approximate Cross-Validation for High-Dimensional GLMs

Leave-one-out cross validation (LOOCV) can be particularly accurate amon...

3 William Stephenson, et al. ∙

research

∙ 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...

0 Brian L Trippe, et al. ∙

research

∙ 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...

3 Raj Agrawal, et al. ∙

research

∙ 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...

20 Miriam Shiffman, et al. ∙

research

∙ 10/15/2018

Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics

A central question in many probabilistic clustering problems is how many...

0 Runjing Liu, et al. ∙

research

∙ 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...

8 Raj Agrawal, et al. ∙

research

∙ 09/25/2018

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach

Bayesian inference typically requires the computation of an approximatio...

24 Jonathan H. Huggins, et al. ∙

research

∙ 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...

2 Jonathan H. Huggins, et al. ∙

research

∙ 06/01/2018

Return of the Infinitesimal Jackknife

The error or variability of machine learning algorithms is often assesse...

0 Ryan Giordano, et al. ∙

research

∙ 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...

0 Raj Agrawal, et al. ∙

research

∙ 02/05/2018

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent

Coherent uncertainty quantification is a key strength of Bayesian method...

0 Trevor Campbell, et al. ∙

research

∙ 12/05/2017

Measuring Cluster Stability for Bayesian Nonparametrics Using the Linear Bootstrap

Clustering procedures typically estimate which data points are clustered...

0 Ryan Giordano, et al. ∙

research

∙ 10/13/2017

Automated Scalable Bayesian Inference via Hilbert Coresets

The automation of posterior inference in Bayesian data analysis has enab...

0 Trevor Campbell, et al. ∙

research

∙ 09/26/2017

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference

Generalized linear models (GLMs) -- such as logistic regression, Poisson...

0 Jonathan H. Huggins, et al. ∙

research

∙ 11/17/2016

Boosting Variational Inference

Variational inference (VI) provides fast approximations of a Bayesian po...

0 Fangjian Guo, et al. ∙

research

∙ 06/23/2016

Fast robustness quantification with variational Bayes

Bayesian hierarchical models are increasing popular in economics. When u...

0 Ryan Giordano, et al. ∙

research

∙ 05/20/2016

Coresets for Scalable Bayesian Logistic Regression

The use of Bayesian methods in large-scale data settings is attractive b...

0 Jonathan H. Huggins, et al. ∙

research

∙ 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...

0 Diana Cai, et al. ∙

research

∙ 03/22/2016

Edge-exchangeable graphs and sparsity

A known failing of many popular random graph models is that the Aldous-H...

0 Tamara Broderick, et al. ∙

research

∙ 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...

0 Ryan Giordano, et al. ∙

research

∙ 02/26/2015

Covariance Matrices and Influence Scores for Mean Field Variational Bayes

Mean field variational Bayes (MFVB) is a popular posterior approximation...

0 Ryan Giordano, et al. ∙

research

∙ 10/24/2014

Covariance Matrices for Mean Field Variational Bayes

Mean Field Variational Bayes (MFVB) is a popular posterior approximation...

0 Ryan Giordano, et al. ∙

research

∙ 10/17/2014

Variational Bayes for Merging Noisy Databases

Bayesian entity resolution merges together multiple, noisy databases and...

0 Tamara Broderick, et al. ∙

research

∙ 07/30/2013

Optimistic Concurrency Control for Distributed Unsupervised Learning

Research on distributed machine learning algorithms has focused primaril...

0 Xinghao Pan, et al. ∙

research

∙ 07/25/2013

Streaming Variational Bayes

We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)syn...

0 Tamara Broderick, et al. ∙

research

∙ 12/10/2012

MAD-Bayes: MAP-based Asymptotic Derivations from Bayes

The classical mixture of Gaussians model is related to K-means via small...

0 Tamara Broderick, et al. ∙

Tamara Broderick

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