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Independent finite approximations for Bayesian nonparametric inference: construction, error bounds, and practical implications

Sep 22, 2020
Tin D. Nguyen, Jonathan Huggins, Lorenzo Masoero, Lester Mackey, Tamara Broderick


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Approximate Cross-Validation with Low-Rank Data in High Dimensions

Aug 24, 2020
William T. Stephenson, Madeleine Udell, Tamara Broderick

* 19 pages, 6 figures 

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Finite mixture models are typically inconsistent for the number of components

Jul 08, 2020
Diana Cai, Trevor Campbell, Tamara Broderick

* 16 pages, 1 figure 

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Approximate Cross-Validation for Structured Models

Jun 23, 2020
Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande, Tamara Broderick

* 22 pages, 7 figures 

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Practical Posterior Error Bounds from Variational Objectives

Oct 31, 2019
Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell, Tamara Broderick

* 22 pages, 2 figures, 1 table, including Appendix. A python package for computing the bounds we develop in this paper is available at https://github.com/jhuggins/viabel 

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A Higher-Order Swiss Army Infinitesimal Jackknife

Jul 28, 2019
Ryan Giordano, Michael I. Jordan, Tamara Broderick


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Sparse Approximate Cross-Validation for High-Dimensional GLMs

May 31, 2019
William Stephenson, Tamara Broderick

* 29 pages, 3 figures 

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LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations

May 17, 2019
Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

* Accepted at ICML 2019 

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The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions

May 16, 2019
Raj Agrawal, Jonathan H. Huggins, Brian Trippe, Tamara Broderick

* Accepted at ICML 2019. 20 pages, 4 figures, 3 tables 

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Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data

Nov 28, 2018
Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick

* 18 pages, 6 figures. Preliminary work appeared in the 2017 NeurIPS workshops in Advances in Approximate Bayesian Inference (http://approximateinference.org/2017) and Machine Learning for Computational Biology (https://mlcb.github.io

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Data-dependent compression of random features for large-scale kernel approximation

Oct 09, 2018
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick

* 22 pages, 6 figures 

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Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees

Oct 04, 2018
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak, Tamara Broderick

* 20 pages, 3 figures 

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

Oct 01, 2018
Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak, Tamara Broderick

* 22 pages, 2 figures 

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Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models

Jun 24, 2018
Raj Agrawal, Tamara Broderick, Caroline Uhler

* Proceedings of the 30th International Conference on Machine Learning. 2018, to appear. 16 pages, 5 figures 

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Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent

May 28, 2018
Trevor Campbell, Tamara Broderick

* Appearing in the 2018 International Conference on Machine Learning (ICML). 13 pages, 7 figures 

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

Nov 13, 2017
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick

* In Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS 2017) 

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Automated Scalable Bayesian Inference via Hilbert Coresets

Oct 13, 2017
Trevor Campbell, Tamara Broderick


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Boosting Variational Inference

Mar 01, 2017
Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B. Dunson

* 17 pages, 7 figures 

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Coresets for Scalable Bayesian Logistic Regression

Feb 06, 2017
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick

* In Proceedings of Advances in Neural Information Processing Systems (NIPS 2016) 

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Edge-exchangeable graphs and sparsity (NIPS 2016)

Feb 03, 2017
Diana Cai, Trevor Campbell, Tamara Broderick

* In the proceedings of the Advances in Neural Information Processing Systems 29 (NIPS), 2016. Preliminary work appeared in the 2015 NIPS workshops on Networks in the Social and Information Sciences (http://stanford.edu/~jugander/NetworksNIPS2015/) and Bayesian Nonparametrics: The Next Generation (https://sites.google.com/site/nipsbnp2015/). 26 pages, 4 figures 

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Fast robustness quantification with variational Bayes

Jun 23, 2016
Ryan Giordano, Tamara Broderick, Rachael Meager, Jonathan Huggins, Michael Jordan

* presented at 2016 ICML Workshop on #Data4Good: Machine Learning in Social Good Applications, New York, NY 

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Completely random measures for modeling power laws in sparse graphs

Mar 22, 2016
Diana Cai, Tamara Broderick

* This paper appeared in the NIPS 2015 Workshop on Networks in the Social and Information Sciences, http://stanford.edu/~jugander/NetworksNIPS2015/ 

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Edge-exchangeable graphs and sparsity

Mar 22, 2016
Tamara Broderick, Diana Cai

* This paper appeared in the NIPS 2015 Workshop on Networks in the Social and Information Sciences, http://stanford.edu/~jugander/NetworksNIPS2015/. An earlier version appeared in the NIPS 2015 Workshop Bayesian Nonparametrics: The Next Generation, https://sites.google.com/site/nipsbnp2015/ 

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Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes

Dec 23, 2015
Ryan Giordano, Tamara Broderick, Michael Jordan

* 21 pages. arXiv admin note: substantial text overlap with arXiv:1502.07685 

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Covariance Matrices and Influence Scores for Mean Field Variational Bayes

Feb 26, 2015
Ryan Giordano, Tamara Broderick

* 28 pages, 5 figures, submitted to ICML 2015 

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Covariance Matrices for Mean Field Variational Bayes

Dec 08, 2014
Ryan Giordano, Tamara Broderick

* 14 pages, 2 figures 

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Variational Bayes for Merging Noisy Databases

Oct 17, 2014
Tamara Broderick, Rebecca C. Steorts

* 12 pages 

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Streaming Variational Bayes

Nov 20, 2013
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan

* 25 pages, 3 figures, 1 table 

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