
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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Approximate CrossValidation with LowRank Data in High Dimensions
Many recent advances in machine learning are driven by a challenging tri...
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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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Approximate CrossValidation for Structured Models
Many modern data analyses benefit from explicitly modeling dependence st...
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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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Practical Posterior Error Bounds from Variational Objectives
Variational inference has become an increasingly attractive, computation...
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A HigherOrder Swiss Army Infinitesimal Jackknife
Cross validation (CV) and the bootstrap are ubiquitous modelagnostic to...
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Local Exchangeability
Exchangeabilityin which the distribution of an infinite sequence is i...
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Sparse Approximate CrossValidation for HighDimensional GLMs
Leaveoneout cross validation (LOOCV) can be particularly accurate amon...
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LRGLM: HighDimensional Bayesian Inference Using LowRank Data Approximations
Due to the ease of modern data collection, applied statisticians often h...
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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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Reconstructing probabilistic trees of cellular differentiation from singlecell RNAseq data
Until recently, transcriptomics was limited to bulk RNA sequencing, obsc...
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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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Datadependent compression of random features for largescale kernel approximation
Kernel methods offer the flexibility to learn complex relationships in m...
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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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Scalable Gaussian Process Inference with Finitedata Mean and Variance Guarantees
Gaussian processes (GPs) offer a flexible class of priors for nonparamet...
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Return of the Infinitesimal Jackknife
The error or variability of machine learning algorithms is often assesse...
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Minimal IMAP MCMC for Scalable Structure Discovery in Causal DAG Models
Learning a Bayesian network (BN) from data can be useful for decisionma...
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Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Coherent uncertainty quantification is a key strength of Bayesian method...
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Measuring Cluster Stability for Bayesian Nonparametrics Using the Linear Bootstrap
Clustering procedures typically estimate which data points are clustered...
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Automated Scalable Bayesian Inference via Hilbert Coresets
The automation of posterior inference in Bayesian data analysis has enab...
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PASSGLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Generalized linear models (GLMs)  such as logistic regression, Poisson...
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Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian po...
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Fast robustness quantification with variational Bayes
Bayesian hierarchical models are increasing popular in economics. When u...
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Coresets for Scalable Bayesian Logistic Regression
The use of Bayesian methods in largescale data settings is attractive b...
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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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Edgeexchangeable graphs and sparsity
A known failing of many popular random graph models is that the AldousH...
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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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Covariance Matrices and Influence Scores for Mean Field Variational Bayes
Mean field variational Bayes (MFVB) is a popular posterior approximation...
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Covariance Matrices for Mean Field Variational Bayes
Mean Field Variational Bayes (MFVB) is a popular posterior approximation...
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Variational Bayes for Merging Noisy Databases
Bayesian entity resolution merges together multiple, noisy databases and...
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Optimistic Concurrency Control for Distributed Unsupervised Learning
Research on distributed machine learning algorithms has focused primaril...
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Streaming Variational Bayes
We present SDABayes, a framework for (S)treaming, (D)istributed, (A)syn...
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MADBayes: MAPbased Asymptotic Derivations from Bayes
The classical mixture of Gaussians model is related to Kmeans via small...
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Combining Spatial and Telemetric Features for Learning Animal Movement Models
We introduce a new graphical model for tracking radiotagged animals and...
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Combinatorial clustering and the beta negative binomial process
We develop a Bayesian nonparametric approach to a general family of late...
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Beta processes, stickbreaking, and power laws
The betaBernoulli process provides a Bayesian nonparametric prior for m...
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Tamara Broderick
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ITT Career Development Assistant Professor at Massachusetts Institute of Technology