
Can we globally optimize crossvalidation loss? Quasiconvexity in ridge regression
Models like LASSO and ridge regression are extensively used in practice ...
read it

For highdimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
Hierarchical Bayesian methods enable information sharing across multiple...
read it

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...
read it

The SKIMFA Kernel: HighDimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
Many scientific problems require identifying a small set of covariates t...
read it

Measuring the sensitivity of Gaussian processes to kernel choice
Gaussian processes (GPs) are used to make medical and scientific decisio...
read it

Optimal transport couplings of Gibbs samplers on partitions for unbiased estimation
Computational couplings of Markov chains provide a practical route to un...
read it

Confidently Comparing Estimators with the cvalue
Modern statistics provides an everexpanding toolkit for estimating unkn...
read it

An Automatic FiniteSample Robustness Metric: Can Dropping a Little Data Change Conclusions?
We propose a method to assess the sensitivity of econometric analyses to...
read it

Independent finite approximations for Bayesian nonparametric inference: construction, error bounds, and practical implications
Bayesian nonparametrics based on completely random measures (CRMs) offer...
read it

Approximate CrossValidation with LowRank Data in High Dimensions
Many recent advances in machine learning are driven by a challenging tri...
read it

Finite mixture models are typically inconsistent for the number of components
Scientists and engineers are often interested in learning the number of ...
read it

Approximate CrossValidation for Structured Models
Many modern data analyses benefit from explicitly modeling dependence st...
read it

More for less: Predicting and maximizing genetic variant discovery via Bayesian nonparametrics
While the cost of sequencing genomes has decreased dramatically in recen...
read it

Practical Posterior Error Bounds from Variational Objectives
Variational inference has become an increasingly attractive, computation...
read it

A HigherOrder Swiss Army Infinitesimal Jackknife
Cross validation (CV) and the bootstrap are ubiquitous modelagnostic to...
read it

Local Exchangeability
Exchangeabilityin which the distribution of an infinite sequence is i...
read it

Sparse Approximate CrossValidation for HighDimensional GLMs
Leaveoneout cross validation (LOOCV) can be particularly accurate amon...
read it

LRGLM: HighDimensional Bayesian Inference Using LowRank Data Approximations
Due to the ease of modern data collection, applied statisticians often h...
read it

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Discovering interaction effects on a response of interest is a fundament...
read it

Reconstructing probabilistic trees of cellular differentiation from singlecell RNAseq data
Until recently, transcriptomics was limited to bulk RNA sequencing, obsc...
read it

Evaluating Sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics
A central question in many probabilistic clustering problems is how many...
read it

Datadependent compression of random features for largescale kernel approximation
Kernel methods offer the flexibility to learn complex relationships in m...
read it

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Bayesian inference typically requires the computation of an approximatio...
read it

Scalable Gaussian Process Inference with Finitedata Mean and Variance Guarantees
Gaussian processes (GPs) offer a flexible class of priors for nonparamet...
read it

Return of the Infinitesimal Jackknife
The error or variability of machine learning algorithms is often assesse...
read it

Minimal IMAP MCMC for Scalable Structure Discovery in Causal DAG Models
Learning a Bayesian network (BN) from data can be useful for decisionma...
read it

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Coherent uncertainty quantification is a key strength of Bayesian method...
read it

Measuring Cluster Stability for Bayesian Nonparametrics Using the Linear Bootstrap
Clustering procedures typically estimate which data points are clustered...
read it

Automated Scalable Bayesian Inference via Hilbert Coresets
The automation of posterior inference in Bayesian data analysis has enab...
read it

PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Generalized linear models (GLMs)  such as logistic regression, Poisson...
read it

Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian po...
read it

Fast robustness quantification with variational Bayes
Bayesian hierarchical models are increasing popular in economics. When u...
read it

Coresets for Scalable Bayesian Logistic Regression
The use of Bayesian methods in largescale data settings is attractive b...
read it

Completely random measures for modeling power laws in sparse graphs
Network data appear in a number of applications, such as online social n...
read it

Edgeexchangeable graphs and sparsity
A known failing of many popular random graph models is that the AldousH...
read it

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
Mean field variational Bayes (MFVB) is a popular posterior approximation...
read it

Covariance Matrices and Influence Scores for Mean Field Variational Bayes
Mean field variational Bayes (MFVB) is a popular posterior approximation...
read it

Covariance Matrices for Mean Field Variational Bayes
Mean Field Variational Bayes (MFVB) is a popular posterior approximation...
read it

Variational Bayes for Merging Noisy Databases
Bayesian entity resolution merges together multiple, noisy databases and...
read it

Optimistic Concurrency Control for Distributed Unsupervised Learning
Research on distributed machine learning algorithms has focused primaril...
read it

Streaming Variational Bayes
We present SDABayes, a framework for (S)treaming, (D)istributed, (A)syn...
read it

MADBayes: MAPbased Asymptotic Derivations from Bayes
The classical mixture of Gaussians model is related to Kmeans via small...
read it

Combining Spatial and Telemetric Features for Learning Animal Movement Models
We introduce a new graphical model for tracking radiotagged animals and...
read it

Combinatorial clustering and the beta negative binomial process
We develop a Bayesian nonparametric approach to a general family of late...
read it

Beta processes, stickbreaking, and power laws
The betaBernoulli process provides a Bayesian nonparametric prior for m...
read it
Tamara Broderick
is this you? claim profile
ITT Career Development Assistant Professor at Massachusetts Institute of Technology