John P. Cunningham

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 76

Top Venues

CoRR

NeurIPS

ICML

NIPS

Publications

Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
Estimating the Hallucination Rate of Generative AI. CoRR (2024)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
Pathologies of Predictive Diversity in Deep Ensembles. Trans. Mach. Learn. Res. 2024 (2024)
Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
Approximation-Aware Bayesian Optimization. CoRR (2024)
Dan Biderman, Jose Javier Gonzalez Ortiz, Jacob Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John P. Cunningham
LoRA Learns Less and Forgets Less. CoRR (2024)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
Pathologies of Predictive Diversity in Deep Ensembles. CoRR (2023)
Yixin Wang, David M. Blei, John P. Cunningham
Posterior Collapse and Latent Variable Non-identifiability. CoRR (2023)
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. CoRR (2023)
Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. NeurIPS (2023)
Luhuan Wu, Geoff Pleiss, John P. Cunningham
Variational nearest neighbor Gaussian process. ICML (2022)
Luhuan Wu, Geoff Pleiss, John P. Cunningham
Variational Nearest Neighbor Gaussian Processes. CoRR (2022)
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
Posterior and Computational Uncertainty in Gaussian Processes. CoRR (2022)
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS (2022)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML (2022)
Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. NeurIPS (2022)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham
On the Normalizing Constant of the Continuous Categorical Distribution. CoRR (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. CoRR (2022)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
Deep Ensembles Work, But Are They Necessary? NeurIPS (2022)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
Deep Ensembles Work, But Are They Necessary? CoRR (2022)
Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham
Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. CoRR (2022)
Yao Fu, John P. Cunningham, Mirella Lapata
Scaling Structured Inference with Randomization. ICML (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. ICBINB (2022)
Rohan Shad, Nicolas Quach, Robyn Fong, Patpilai Kasinpila, Cayley Bowles, Kate M. Callon, Michelle C. Li, Jeffrey J. Teuteberg, John P. Cunningham, Curtis P. Langlotz, William Hiesinger
Simulating time to event prediction with spatiotemporal echocardiography deep learning. CoRR (2021)
Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
A general linear-time inference method for Gaussian Processes on one dimension. J. Mach. Learn. Res. 22 (2021)
Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR (2021)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR (2021)
Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham
Learning sparse log-ratios for high-throughput sequencing data. Bioinform. 38 (1) (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. CoRR (2021)
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
Bias-Free Scalable Gaussian Processes via Randomized Truncations. CoRR (2021)
Rohan Shad, John P. Cunningham, Euan A. Ashley, Curtis P. Langlotz, William Hiesinger
Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. Nat. Mach. Intell. 3 (11) (2021)
Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. NeurIPS (2021)
Matthew R. Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, Estefany Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J. Miska, Jean-Paul Noel, Erica Rodriguez, Michael Schartner, Karolina Socha, Anne E. Urai, C. Daniel Salzman, International Brain Laboratory, John P. Cunningham, Liam Paninski
Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS Comput. Biol. 17 (9) (2021)
Rohan Shad, John P. Cunningham, Euan A. Ashley, Curtis P. Langlotz, William Hiesinger
Medical Imaging and Machine Learning. CoRR (2021)
Geoff Pleiss, John P. Cunningham
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. CoRR (2021)
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
Bias-Free Scalable Gaussian Processes via Randomized Truncations. ICML (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 J. Teuteberg, John P. Cunningham, Curtis P. Langlotz, William Hiesinger
Predicting post-operative right ventricular failure using video-based deep learning. CoRR (2021)
Geoff Pleiss, John P. Cunningham
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. NeurIPS (2021)
Yixin Wang, David M. Blei, John P. Cunningham
Posterior Collapse and Latent Variable Non-identifiability. NeurIPS (2021)
Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
General linear-time inference for Gaussian Processes on one dimension. CoRR (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. CoRR (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
The continuous categorical: a novel simplex-valued exponential family. CoRR (2020)
Anqi Wu, Estefany Kelly Buchanan, Matthew R. 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, International Brain Laboratory, John P. Cunningham, Liam Paninski
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. NeurIPS (2020)
Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. NeurIPS (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. ICBINB@NeurIPS (2020)
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. 21 (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
The continuous categorical: a novel simplex-valued exponential family. ICML (2020)
Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. NeurIPS (2020)
Yao Fu, Yansong Feng, John P. Cunningham
Paraphrase Generation with Latent Bag of Words. CoRR (2020)
Andrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. ICML (2019)
Gabriel Loaiza-Ganem, John P. Cunningham
The continuous Bernoulli: fixing a pervasive error in variational autoencoders. CoRR (2019)
Gabriel Loaiza-Ganem, John P. Cunningham
Deep Random Splines for Point Process Intensity Estimation. CoRR (2019)
Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data. NeurIPS (2019)
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
Calibrating Deep Convolutional Gaussian Processes. AISTATS (2019)
Gabriel Loaiza-Ganem, John P. Cunningham
The continuous Bernoulli: fixing a pervasive error in variational autoencoders. NeurIPS (2019)
Gabriel Loaiza-Ganem, John P. Cunningham
Deep Random Splines for Point Process Intensity Estimation. DGS@ICLR (2019)
Sean R. Bittner, John P. Cunningham
Approximating exponential family models (not single distributions) with a two-network architecture. CoRR (2019)
Yao Fu, Yansong Feng, John P. Cunningham
Paraphrase Generation with Latent Bag of Words. NeurIPS (2019)
Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. CoRR (2019)
Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. NeurIPS (2019)
Andrea Insabato, John P. Cunningham, Matthieu Gilson
Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity. CoRR (2018)
Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
Calibrating Deep Convolutional Gaussian Processes. CoRR (2018)
Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan
A Probabilistic Model of Cardiac Physiology and Electrocardiograms. CoRR (2018)
Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS (2018)
Daniel Hernández, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John P. Cunningham, Liam Paninski
A Novel Variational Family for Hidden Nonlinear Markov Models. CoRR (2018)
Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham
Maximum Entropy Flow Networks. ICLR (Poster) (2017)
Gonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Pawel Hottowy, Alan M. Litke, John P. Cunningham, E. J. Chichilnisky, Liam Paninski
Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput. Biol. 13 (11) (2017)
Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
Sparse probit linear mixed model. Mach. Learn. 106 (9-10) (2017)
Francois Fagan, Jalaj Bhandari, John P. Cunningham
Annular Augmentation Sampling. AISTATS (2017)
Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi
Bayesian Learning of Kernel Embeddings. UAI (2016)
Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham
Linear dynamical neural population models through nonlinear embeddings. NIPS (2016)
Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone
Preconditioning Kernel Matrices. ICML (2016)
Benjamin Bloem-Reddy, John P. Cunningham
Slice Sampling on Hamiltonian Trajectories. ICML (2016)
Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. 12 (5) (2016)
Uygar Sümbül, Douglas H. Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski
Automated scalable segmentation of neurons from multispectral images. NIPS (2016)
Jeffrey S. Seely, Matthew T. Kaufman, Stephen I. Ryu, Krishna V. Shenoy, John P. Cunningham, Mark M. Churchland
Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1. PLoS Comput. Biol. 12 (11) (2016)
Francois Fagan, Jalaj Bhandari, John P. Cunningham
Elliptical Slice Sampling with Expectation Propagation. UAI (2016)
John P. Cunningham, Zoubin Ghahramani
Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16 (2015)
Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
Sparse Estimation in a Correlated Probit Model. CoRR (2015)
Yuanjun Gao, Lars Buesing, Krishna V. Shenoy, John P. Cunningham
High-dimensional neural spike train analysis with generalized count linear dynamical systems. NIPS (2015)
Elad Gilboa, Yunus Saatci, John P. Cunningham
Scaling Multidimensional Inference for Structured Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Jacob R. Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
Bayesian Active Model Selection with an Application to Automated Audiometry. NIPS (2015)
Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
Psychophysical Detection Testing with Bayesian Active Learning. UAI (2015)
Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
Neuroprosthetic decoder training as imitation learning. CoRR (2015)
Josh Merel, Donald M. Pianto, John P. Cunningham, Liam Paninski
Encoder-Decoder Optimization for Brain-Computer Interfaces. PLoS Comput. Biol. 11 (6) (2015)
Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham
Bayesian Optimization with Inequality Constraints. ICML (2014)
Andrew Gordon Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai
Fast Kernel Learning for Multidimensional Pattern Extrapolation. NIPS (2014)
Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
Clustered factor analysis of multineuronal spike data. NIPS (2014)
Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham
GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes. CoRR (2013)
Elad Gilboa, Yunus Saatçi, John P. Cunningham
Scaling Multidimensional Gaussian Processes using Projected Additive Approximations. ICML (1) (2013)
Vikash Gilja, Paul Nuyujukian, Cindy A. Chestek, John P. Cunningham, Byron M. Yu, Joline M. Fan, Stephen I. Ryu, Krishna V. Shenoy
A brain machine interface control algorithm designed from a feedback control perspective. EMBC (2012)
Mengyuan Zhao, Aaron P. Batista, John P. Cunningham, Cynthia A. Chestek, Zuley Rivera-Alvidrez, Rachel S. Kalmar, Stephen I. Ryu, Krishna V. Shenoy, Satish Iyengar
An L 1-regularized logistic model for detecting short-term neuronal interactions. J. Comput. Neurosci. 32 (3) (2012)
John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen
Gaussian Processes for time-marked time-series data. AISTATS (2012)
Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Empirical models of spiking in neural populations. NIPS (2011)
Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Dynamical segmentation of single trials from population neural data. NIPS (2011)
Catie Chang, John P. Cunningham, Gary H. Glover
Influence of heart rate on the BOLD signal: The cardiac response function. NeuroImage 44 (3) (2009)
John P. Cunningham, Vikash Gilja, Stephen I. Ryu, Krishna V. Shenoy
Methods for estimating neural firing rates, and their application to brain-machine interfaces. Neural Networks 22 (9) (2009)
Matthias W. Seeger, Suvrit Sra, John P. Cunningham
Workshop summary: Numerical mathematics in machine learning. ICML (2009)
John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
Fast Gaussian process methods for point process intensity estimation. ICML (2008)
Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. NIPS (2008)
Byron M. Yu, John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models. ICONIP (1) (2007)

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Publication Activity (10 Years)

Top Topics

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John P. Cunningham