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Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning


Jul 01, 2021
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner


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The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective


Jun 11, 2021
Geoff Pleiss, John P. Cunningham


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Rectangular Flows for Manifold Learning


Jun 02, 2021
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham


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Simulating time to event prediction with spatiotemporal echocardiography deep learning


Mar 03, 2021
Rohan Shad, Nicolas Quach, Robyn Fong, Patpilai Kasinpila, Cayley Bowles, Kate M. Callon, Michelle C. Li, Jeffrey Teuteberg, John P. Cunningham, Curtis P. Langlotz, William Hiesinger

* 9 pages, 5 figures 

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Medical Imaging and Machine Learning


Mar 02, 2021
Rohan Shad, John P. Cunningham, Euan A. Ashley, Curtis P. Langlotz, William Hiesinger

* 9 pages, 4 figures 

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Predicting post-operative right ventricular failure using video-based deep learning


Feb 28, 2021
Rohan Shad, Nicolas Quach, Robyn Fong, Patpilai Kasinpila, Cayley Bowles, Miguel Castro, Ashrith Guha, Eddie Suarez, Stefan Jovinge, Sangjin Lee, Theodore Boeve, Myriam Amsallem, Xiu Tang, Francois Haddad, Yasuhiro Shudo, Y. Joseph Woo, Jeffrey Teuteberg, John P. Cunningham, Curt P. Langlotz, William Hiesinger

* 12 pages, 3 figures 

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Bias-Free Scalable Gaussian Processes via Randomized Truncations


Feb 12, 2021
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham


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Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning


Nov 10, 2020
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham


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General linear-time inference for Gaussian Processes on one dimension


Mar 11, 2020
Jackson Loper, David Blei, John P. Cunningham, Liam Paninski

* 36 pages, 5 figures, submitted to ICML 

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The continuous categorical: a novel simplex-valued exponential family


Feb 20, 2020
Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, John P. Cunningham


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Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax


Feb 07, 2020
Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham


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Paraphrase Generation with Latent Bag of Words


Jan 07, 2020
Yao Fu, Yansong Feng, John P. Cunningham

* NeurIPS 19 camera ready 

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The continuous Bernoulli: fixing a pervasive error in variational autoencoders


Jul 23, 2019
Gabriel Loaiza-Ganem, John P. Cunningham


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Approximating exponential family models (not single distributions) with a two-network architecture


Mar 18, 2019
Sean R. Bittner, John P. Cunningham


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Deep Random Splines for Point Process Intensity Estimation


Mar 06, 2019
Gabriel Loaiza-Ganem, John P. Cunningham


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A Probabilistic Model of Cardiac Physiology and Electrocardiograms


Dec 01, 2018
Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200 

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Calibrating Deep Convolutional Gaussian Processes


May 26, 2018
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone

* 12 pages 

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Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity


May 25, 2018
Andrea Insabato, John P. Cunningham, Matthieu Gilson


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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data


Mar 10, 2018
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert

* Updated. 31 pages (+ appendix) 

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Reparameterizing the Birkhoff Polytope for Variational Permutation Inference


Oct 26, 2017
Scott W. Linderman, Gonzalo E. Mena, Hal Cooper, Liam Paninski, John P. Cunningham


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Sparse Probit Linear Mixed Model


Jul 17, 2017
Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft

* Machine Learning, 106(9), 1621-1642 (2017) 
* Published version, 21 pages, 6 figures 

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Maximum Entropy Flow Networks


Apr 28, 2017
Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham

* Accepted at ICLR 2017 

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Linear dynamical neural population models through nonlinear embeddings


Oct 25, 2016
Yuanjun Gao, Evan Archer, Liam Paninski, John P. Cunningham

* NIPS 2016 

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Bayesian Learning of Kernel Embeddings


Jun 02, 2016
Seth Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi

* Conference paper appearing in UAI 2016, including Appendix 

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Preconditioning Kernel Matrices


May 25, 2016
Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone


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Linear Dimensionality Reduction: Survey, Insights, and Generalizations


Mar 18, 2016
John P. Cunningham, Zoubin Ghahramani

* Journal of Machine Learning Research. 16(Dec): 2859-2900, 2015 
* 42 pages, 5 figures, 1 table 

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Neuroprosthetic decoder training as imitation learning


Mar 14, 2016
Josh Merel, David Carlson, Liam Paninski, John P. Cunningham


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GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes


Dec 31, 2013
Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham

* 13 Pages, 9 Figures, 1 Table. Submitted for publication 

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