ML Anthology

Cunningham, John P

42 publications

NeurIPS 2024 Approximation-Aware Bayesian Optimization Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
NeurIPS 2024 Computation-Aware Gaussian Processes: Model Selection and Linear-Time Inference Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
NeurIPS 2024 Estimating the Hallucination Rate of Generative AI Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David Blei
NeurIPS 2023 Practical and Asymptotically Exact Conditional Sampling in Diffusion Models Luhuan Wu, Brian Trippe, Christian Naesseth, David M. Blei, John P. Cunningham
NeurIPS 2022 Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome Elliott Gordon-Rodriguez, Thomas Quinn, John P. Cunningham
NeurIPS 2022 Deep Ensembles Work, but Are They Necessary? Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
NeurIPS 2022 Posterior and Computational Uncertainty in Gaussian Processes Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
ICML 2022 Variational Nearest Neighbor Gaussian Process Luhuan Wu, Geoff Pleiss, John P Cunningham
JMLR 2021 A General Linear-Time Inference Method for Gaussian Processes on One Dimension Jackson Loper, David Blei, John P. Cunningham, Liam Paninski
ICML 2021 Bias-Free Scalable Gaussian Processes via Randomized Truncations Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P Cunningham
NeurIPS 2021 Posterior Collapse and Latent Variable Non-Identifiability Yixin Wang, David M. Blei, John P. Cunningham
NeurIPS 2021 Rectangular Flows for Manifold Learning Anthony L Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
NeurIPS 2021 The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss, John P. Cunningham
NeurIPS 2020 Deep Graph Pose: A Semi-Supervised Deep Graphical Model for Improved Animal Pose Tracking Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory The International Brain Laboratory, John P. Cunningham, Liam Paninski
JMLR 2020 Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert
NeurIPS 2020 Invertible Gaussian Reparameterization: Revisiting the Gumbel-SoftMax Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
NeurIPS 2020 Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations Joshua Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott Linderman
NeurIPS 2019 BehaveNet: Nonlinear Embedding and Bayesian Neural Decoding of Behavioral Videos Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R Datta, Scott Linderman, Liam Paninski
ICLRW 2019 Deep Random Splines for Point Process Intensity Estimation Gabriel Loaiza-Ganem, John P. Cunningham
NeurIPS 2019 Deep Random Splines for Point Process Intensity Estimation of Neural Population Data Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark Churchland, John P. Cunningham
NeurIPS 2019 Paraphrase Generation with Latent Bag of Words Yao Fu, Yansong Feng, John P. Cunningham
NeurIPS 2019 The Continuous Bernoulli: Fixing a Pervasive Error in Variational Autoencoders Gabriel Loaiza-Ganem, John P. Cunningham
AISTATS 2018 Reparameterizing the Birkhoff Polytope for Variational Permutation Inference Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
AISTATS 2017 Annular Augmentation Sampling Francois Fagan, Jalaj Bhandari, John P. Cunningham
ICLR 2017 Maximum Entropy Flow Networks Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham
MLJ 2017 Sparse Probit Linear Mixed Model Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
NeurIPS 2016 Automated Scalable Segmentation of Neurons from Multispectral Images Uygar Sümbül, Douglas Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward Boyden, Liam Paninski
UAI 2016 Bayesian Learning of Kernel Embeddings Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi
UAI 2016 Elliptical Slice Sampling with Expectation Propagation Francois Fagan, Jalaj Bhandari, John P. Cunningham
NeurIPS 2016 Linear Dynamical Neural Population Models Through Nonlinear Embeddings Yuanjun Gao, Evan W Archer, Liam Paninski, John P. Cunningham
NeurIPS 2015 Bayesian Active Model Selection with an Application to Automated Audiometry Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham
NeurIPS 2015 High-Dimensional Neural Spike Train Analysis with Generalized Count Linear Dynamical Systems Yuanjun Gao, Lars Busing, Krishna V. Shenoy, John P. Cunningham
JMLR 2015 Linear Dimensionality Reduction: Survey, Insights, and Generalizations John P. Cunningham, Zoubin Ghahramani
UAI 2015 Psychophysical Detection Testing with Bayesian Active Learning Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
NeurIPS 2014 Clustered Factor Analysis of Multineuronal Spike Data Lars Buesing, Timothy A Machado, John P. Cunningham, Liam Paninski
NeurIPS 2014 Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew G Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham
NeurIPS 2011 Dynamical Segmentation of Single Trials from Population Neural Data Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
NeurIPS 2011 Empirical Models of Spiking in Neural Populations Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
ICML 2009 Workshop Summary: Numerical Mathematics in Machine Learning Matthias W. Seeger, Suvrit Sra, John P. Cunningham
ICML 2008 Fast Gaussian Process Methods for Point Process Intensity Estimation John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
NeurIPS 2008 Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
NeurIPS 2007 Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani