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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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Slice Sampling for General Completely Random Measures

Jun 25, 2020
Peiyuan Zhu, Alexandre Bouchard-Côté, Trevor Campbell


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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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Sparse Variational Inference: Bayesian Coresets from Scratch

Jun 07, 2019
Trevor Campbell, Boyan Beronov


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Universal Boosting Variational Inference

Jun 04, 2019
Trevor Campbell, Xinglong Li


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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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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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Automated Scalable Bayesian Inference via Hilbert Coresets

Oct 13, 2017
Trevor Campbell, Tamara Broderick


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Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models

Jul 26, 2017
Trevor Campbell, Brian Kulis, Jonathan How

* 27 pages 

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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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Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures

Nov 22, 2016
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III


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Small-Variance Nonparametric Clustering on the Hypersphere

Jul 21, 2016
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III

* IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 334-342). (2015) 

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Streaming, Distributed Variational Inference for Bayesian Nonparametrics

Oct 30, 2015
Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How

* This paper was presented at NIPS 2015. Please use the following BibTeX citation: @inproceedings{Campbell15_NIPS, Author = {Trevor Campbell and Julian Straub and John W. {Fisher III} and Jonathan P. How}, Title = {Streaming, Distributed Variational Inference for Bayesian Nonparametrics}, Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year = {2015}} 

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Approximate Decentralized Bayesian Inference

Jun 12, 2014
Trevor Campbell, Jonathan P. How

* This paper was presented at UAI 2014. Please use the following BibTeX citation: @inproceedings{Campbell14_UAI, Author = {Trevor Campbell and Jonathan P. How}, Title = {Approximate Decentralized Bayesian Inference}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}} 

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Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

Nov 01, 2013
Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

* This paper is from NIPS 2013. Please use the following BibTeX citation: @inproceedings{Campbell13_NIPS, Author = {Trevor Campbell and Miao Liu and Brian Kulis and Jonathan P. How and Lawrence Carin}, Title = {Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process}, Booktitle = {Advances in Neural Information Processing Systems (NIPS)}, Year = {2013}} 

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