John P. Cunningham

According to our database, John P. Cunningham authored at least 85 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Pathologies of Predictive Diversity in Deep Ensembles.

Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham

Trans. Mach. Learn. Res., 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 M. Blei

CoRR, 2024

Approximation-Aware Bayesian Optimization.

Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner

CoRR, 2024

LoRA Learns Less and Forgets Less.

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

CoRR, 2024

2023

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models.

Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

On the Normalizing Constant of the Continuous Categorical Distribution.

Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham

CoRR, 2022

Variational Nearest Neighbor Gaussian Processes.

Luhuan Wu, Geoff Pleiss, John P. Cunningham

CoRR, 2022

Posterior and Computational Uncertainty in Gaussian Processes.

Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome.

Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep Ensembles Work, But Are They Necessary?

Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Variational nearest neighbor Gaussian process.

Luhuan Wu, Geoff Pleiss, John P. Cunningham

Proceedings of the International Conference on Machine Learning, 2022

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization.

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

Proceedings of the International Conference on Machine Learning, 2022

Scaling Structured Inference with Randomization.

Yao Fu, John P. Cunningham, Mirella Lapata

Proceedings of the International Conference on Machine Learning, 2022

Denoising Deep Generative Models.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini

Proceedings of the Proceedings on "I Can't Believe It's Not Better!, 2022

2021

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

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

PLoS Comput. Biol., 2021

Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging.

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

Nat. Mach. Intell., 2021

A general linear-time inference method for Gaussian Processes on one dimension.

Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski

J. Mach. Learn. Res., 2021

Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning.

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

CoRR, 2021

Simulating time to event prediction with spatiotemporal echocardiography deep learning.

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

CoRR, 2021

Medical Imaging and Machine Learning.

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

CoRR, 2021

Hierarchical Inducing Point Gaussian Process for Inter-domain Observations.

Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham

CoRR, 2021

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

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

CoRR, 2021

Learning sparse log-ratios for high-throughput sequencing data.

Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham

Bioinform., 2021

Posterior Collapse and Latent Variable Non-identifiability.

Yixin Wang, David M. Blei, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Geoff Pleiss, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Rectangular Flows for Manifold Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bias-Free Scalable Gaussian Processes via Randomized Truncations.

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

Proceedings of the 38th International Conference on Machine Learning, 2021

Hierarchical Inducing Point Gaussian Process for Inter-domian Observations.

Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

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

J. Mach. Learn. Res., 2020

General linear-time inference for Gaussian Processes on one dimension.

Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski

CoRR, 2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking.

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 E. Angelaki, Andrés Bendesky, International Brain Laboratory, John P. Cunningham, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax.

Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations.

Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The continuous categorical: a novel simplex-valued exponential family.

Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019

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

Sean R. Bittner, John P. Cunningham

CoRR, 2019

Deep Random Splines for Point Process Intensity Estimation of Neural Population Data.

Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The continuous Bernoulli: fixing a pervasive error in variational autoencoders.

Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Paraphrase Generation with Latent Bag of Words.

Yao Fu, Yansong Feng, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos.

Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey E. Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography.

Andrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan

Proceedings of the 36th International Conference on Machine Learning, 2019

Deep Random Splines for Point Process Intensity Estimation.

Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Calibrating Deep Convolutional Gaussian Processes.

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

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

A Probabilistic Model of Cardiac Physiology and Electrocardiograms.

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

CoRR, 2018

A Novel Variational Family for Hidden Nonlinear Markov Models.

Daniel Hernández, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John P. Cunningham, Liam Paninski

CoRR, 2018

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

Andrea Insabato, John P. Cunningham, Matthieu Gilson

CoRR, 2018

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference.

Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

Gonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Pawel Hottowy, Alan M. Litke, John P. Cunningham, E. J. Chichilnisky, Liam Paninski

PLoS Comput. Biol., 2017

Sparse probit linear mixed model.

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

Mach. Learn., 2017

Maximum Entropy Flow Networks.

Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham

Proceedings of the 5th International Conference on Learning Representations, 2017

Annular Augmentation Sampling.

Francois Fagan, Jalaj Bhandari, John P. Cunningham

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1.

Jeffrey S. Seely, Matthew T. Kaufman, Stephen I. Ryu, Krishna V. Shenoy, John P. Cunningham, Mark M. Churchland

PLoS Comput. Biol., 2016

Neuroprosthetic Decoder Training as Imitation Learning.

Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham

PLoS Comput. Biol., 2016

Bayesian Learning of Kernel Embeddings.

Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Elliptical Slice Sampling with Expectation Propagation.

Francois Fagan, Jalaj Bhandari, John P. Cunningham

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Automated scalable segmentation of neurons from multispectral images.

Uygar Sümbül, Douglas H. Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Linear dynamical neural population models through nonlinear embeddings.

Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Preconditioning Kernel Matrices.

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

Proceedings of the 33nd International Conference on Machine Learning, 2016

Slice Sampling on Hamiltonian Trajectories.

Benjamin Bloem-Reddy, John P. Cunningham

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Encoder-Decoder Optimization for Brain-Computer Interfaces.

Josh Merel, Donald M. Pianto, John P. Cunningham, Liam Paninski

PLoS Comput. Biol., 2015

Scaling Multidimensional Inference for Structured Gaussian Processes.

Elad Gilboa, Yunus Saatci, John P. Cunningham

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Linear dimensionality reduction: survey, insights, and generalizations.

John P. Cunningham, Zoubin Ghahramani

J. Mach. Learn. Res., 2015

Sparse Estimation in a Correlated Probit Model.

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

CoRR, 2015

Psychophysical Detection Testing with Bayesian Active Learning.

Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Bayesian Active Model Selection with an Application to Automated Audiometry.

Jacob R. Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

High-dimensional neural spike train analysis with generalized count linear dynamical systems.

Yuanjun Gao, Lars Buesing, Krishna V. Shenoy, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Fast Kernel Learning for Multidimensional Pattern Extrapolation.

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

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Clustered factor analysis of multineuronal spike data.

Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Bayesian Optimization with Inequality Constraints.

Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes.

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

CoRR, 2013

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations.

Elad Gilboa, Yunus Saatçi, John P. Cunningham

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Gaussian Processes for time-marked time-series data.

John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

An L 1-regularized logistic model for detecting short-term neuronal interactions.

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

J. Comput. Neurosci., 2012

A brain machine interface control algorithm designed from a feedback control perspective.

Vikash Gilja, Paul Nuyujukian, Cindy A. Chestek, John P. Cunningham, Byron M. Yu, Joline M. Fan, Stephen I. Ryu, Krishna V. Shenoy

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

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

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Empirical models of spiking in neural populations.

Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2009

Methods for estimating neural firing rates, and their application to brain-machine interfaces.

John P. Cunningham, Vikash Gilja, Stephen I. Ryu, Krishna V. Shenoy

Neural Networks, 2009

Influence of heart rate on the BOLD signal: The cardiac response function.

Catie Chang, John P. Cunningham, Gary H. Glover

NeuroImage, 2009

Workshop summary: Numerical mathematics in machine learning.

Matthias W. Seeger, Suvrit Sra, John P. Cunningham

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

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

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Fast Gaussian process methods for point process intensity estimation.

John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani

Proceedings of the Machine Learning, 2008

2007

Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes.

John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models.

Byron M. Yu, John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani

Proceedings of the Neural Information Processing, 14th International Conference, 2007

2006

Increasing the Performance of Cortically-Controlled Prostheses.

Krishna V. Shenoy, Teresa H. Santhanam, Stephen I. Ryu, Afsheen Afshar, Byron M. Yu, Vikash Gilja, Michael D. Linderman, Rachel S. Kalmar, John P. Cunningham, Caleb T. Kemere, Aaron P. Batista, Mark M. Churchland, Teresa H. Meng

Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

Optimal Target Placement for Neural Communication Prostheses.

John P. Cunningham, Byron M. Yu, Krishna V. Shenoy

Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006