John P. Cunningham

111 papers A* 28A 8Journal 56Unranked 19

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Bayesian Invariance Modeling of Multi-Environment Data. CoRR Luhuan Wu, Mingzhang Yin, Yixin Wang, John P. Cunningham, David M. Blei
2025	J	`jnl`	Scalable Differentially Private Bayesian Optimization. CoRR Getoar Sopa, Juraj Marusic, Marco Avella-Medina, John P. Cunningham
2025	J	`jnl`	Variational Deep Learning via Implicit Regularization. CoRR Jonathan Wenger, Beau Coker, Juraj Marusic, John P. Cunningham
2024	A*	`conf`	Approximation-Aware Bayesian Optimization. NeurIPS Natalie Maus, Kyurae Kim, David Eriksson, Geoff Pleiss, John P. Cunningham, Jacob R. Gardner
2024	J	`jnl`	Approximation-Aware Bayesian Optimization. CoRR Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
2024	A*	`conf`	Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. NeurIPS Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
2024	J	`jnl`	Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. CoRR Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
2024	A*	`conf`	Estimating the Hallucination Rate of Generative AI. NeurIPS Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	J	`jnl`	Estimating the Hallucination Rate of Generative AI. CoRR Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei
2024	J	`jnl`	LoRA Learns Less and Forgets Less. CoRR Dan Biderman, Jose Javier Gonzalez Ortiz, Jacob P. Portes, Mansheej Paul, Philip Greengard, Connor Jennings, Daniel King, Sam Havens, Vitaliy Chiley, Jonathan Frankle, Cody Blakeney, John P. Cunningham
2024	J	`jnl`	Pathologies of Predictive Diversity in Deep Ensembles. Trans. Mach. Learn. Res. Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
2024	J	`jnl`	Theoretical Limitations of Ensembles in the Age of Overparameterization. CoRR Niclas Dern, John P. Cunningham, Geoff Pleiss
2023	J	`jnl`	Pathologies of Predictive Diversity in Deep Ensembles. CoRR Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
2023	J	`jnl`	Posterior Collapse and Latent Variable Non-identifiability. CoRR Yixin Wang, David M. Blei, John P. Cunningham
2023	A*	`conf`	Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. NeurIPS Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
2023	J	`jnl`	Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. CoRR Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham
2022	A*	`conf`	Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. NeurIPS Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham
2022	J	`jnl`	Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. CoRR Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham
2022	A*	`conf`	Deep Ensembles Work, But Are They Necessary? NeurIPS Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
2022	J	`jnl`	Deep Ensembles Work, But Are They Necessary? CoRR Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
2022	—	`conf`	Denoising Deep Generative Models. ICBINB Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
2022	J	`jnl`	Denoising Deep Generative Models. CoRR Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
2022	J	`jnl`	On the Normalizing Constant of the Continuous Categorical Distribution. CoRR Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham
2022	A*	`conf`	Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
2022	J	`jnl`	Posterior and Computational Uncertainty in Gaussian Processes. CoRR Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
2022	A*	`conf`	Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
2022	A*	`conf`	Scaling Structured Inference with Randomization. ICML Yao Fu, John P. Cunningham, Mirella Lapata
2022	J	`jnl`	Variational Nearest Neighbor Gaussian Processes. CoRR Luhuan Wu, Geoff Pleiss, John P. Cunningham
2022	A*	`conf`	Variational nearest neighbor Gaussian process. ICML Luhuan Wu, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	A general linear-time inference method for Gaussian Processes on one dimension. J. Mach. Learn. Res. Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
2021	A*	`conf`	Bias-Free Scalable Gaussian Processes via Randomized Truncations. ICML Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Bias-Free Scalable Gaussian Processes via Randomized Truncations. CoRR Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. Nat. Mach. Intell. Rohan Shad, John P. Cunningham, Euan A. Ashley, Curtis P. Langlotz, William Hiesinger
2021	J	`jnl`	Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
2021	A	`conf`	Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
2021	J	`jnl`	Learning sparse log-ratios for high-throughput sequencing data. Bioinform. Elliott Gordon-Rodríguez, Thomas P. Quinn, John P. Cunningham
2021	J	`jnl`	Medical Imaging and Machine Learning. CoRR Rohan Shad, John P. Cunningham, Euan A. Ashley, Curtis P. Langlotz, William Hiesinger
2021	J	`jnl`	Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS Comput. Biol. 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
2021	A*	`conf`	Posterior Collapse and Latent Variable Non-identifiability. NeurIPS Yixin Wang, David M. Blei, John P. Cunningham
2021	J	`jnl`	Predicting post-operative right ventricular failure using video-based deep learning. CoRR 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
2021	A*	`conf`	Rectangular Flows for Manifold Learning. NeurIPS Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Rectangular Flows for Manifold Learning. CoRR Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
2021	J	`jnl`	Simulating time to event prediction with spatiotemporal echocardiography deep learning. CoRR 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
2021	A*	`conf`	The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. NeurIPS Geoff Pleiss, John P. Cunningham
2021	J	`jnl`	The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. CoRR Geoff Pleiss, John P. Cunningham
2020	A*	`conf`	Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. NeurIPS 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
2020	J	`jnl`	Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. J. Mach. Learn. Res. Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian P. Robert
2020	J	`jnl`	General linear-time inference for Gaussian Processes on one dimension. CoRR Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
2020	A*	`conf`	Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. NeurIPS Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
2020	J	`jnl`	Paraphrase Generation with Latent Bag of Words. CoRR Yao Fu, Yansong Feng, John P. Cunningham
2020	A*	`conf`	Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. NeurIPS Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
2020	A*	`conf`	The continuous categorical: a novel simplex-valued exponential family. ICML Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
2020	J	`jnl`	The continuous categorical: a novel simplex-valued exponential family. CoRR Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
2020	—	`conf`	Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. ICBINB@NeurIPS Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2020	J	`jnl`	Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. CoRR Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
2019	J	`jnl`	Approximating exponential family models (not single distributions) with a two-network architecture. CoRR Sean R. Bittner, John P. Cunningham
2019	A*	`conf`	BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. NeurIPS 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
2019	A	`conf`	Calibrating Deep Convolutional Gaussian Processes. AISTATS Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
2019	A*	`conf`	Deep Random Splines for Point Process Intensity Estimation of Neural Population Data. NeurIPS Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham
2019	—	`conf`	Deep Random Splines for Point Process Intensity Estimation. DGS@ICLR Gabriel Loaiza-Ganem, John P. Cunningham
2019	J	`jnl`	Deep Random Splines for Point Process Intensity Estimation. CoRR Gabriel Loaiza-Ganem, John P. Cunningham
2019	A*	`conf`	Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. ICML Andrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan
2019	J	`jnl`	Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. CoRR Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
2019	A*	`conf`	Paraphrase Generation with Latent Bag of Words. NeurIPS Yao Fu, Yansong Feng, John P. Cunningham
2019	A*	`conf`	The continuous Bernoulli: fixing a pervasive error in variational autoencoders. NeurIPS Gabriel Loaiza-Ganem, John P. Cunningham
2019	J	`jnl`	The continuous Bernoulli: fixing a pervasive error in variational autoencoders. CoRR Gabriel Loaiza-Ganem, John P. Cunningham
2018	J	`jnl`	A Novel Variational Family for Hidden Nonlinear Markov Models. CoRR Daniel Hernández, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John P. Cunningham, Liam Paninski
2018	J	`jnl`	A Probabilistic Model of Cardiac Physiology and Electrocardiograms. CoRR Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan
2018	J	`jnl`	Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity. CoRR Andrea Insabato, John P. Cunningham, Matthieu Gilson
2018	J	`jnl`	Calibrating Deep Convolutional Gaussian Processes. CoRR Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone
2018	A	`conf`	Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
2017	A	`conf`	Annular Augmentation Sampling. AISTATS Francois Fagan, Jalaj Bhandari, John P. Cunningham
2017	J	`jnl`	Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput. Biol. Gonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Pawel Hottowy, Alan M. Litke, John P. Cunningham, E. J. Chichilnisky, Liam Paninski
2017	—	`conf`	Maximum Entropy Flow Networks. ICLR (Poster) Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham
2017	J	`jnl`	Sparse probit linear mixed model. Mach. Learn. Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
2016	—	`conf`	Automated scalable segmentation of neurons from multispectral images. NIPS Uygar Sümbül, Douglas H. Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski
2016	A	`conf`	Bayesian Learning of Kernel Embeddings. UAI Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi
2016	A	`conf`	Elliptical Slice Sampling with Expectation Propagation. UAI Francois Fagan, Jalaj Bhandari, John P. Cunningham
2016	—	`conf`	Linear dynamical neural population models through nonlinear embeddings. NIPS Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham
2016	J	`jnl`	Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
2016	A*	`conf`	Preconditioning Kernel Matrices. ICML Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone
2016	A*	`conf`	Slice Sampling on Hamiltonian Trajectories. ICML Benjamin Bloem-Reddy, John P. Cunningham
2016	J	`jnl`	Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1. PLoS Comput. Biol. Jeffrey S. Seely, Matthew T. Kaufman, Stephen I. Ryu, Krishna V. Shenoy, John P. Cunningham, Mark M. Churchland
2015	—	`conf`	Bayesian Active Model Selection with an Application to Automated Audiometry. NIPS Jacob R. Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
2015	J	`jnl`	Encoder-Decoder Optimization for Brain-Computer Interfaces. PLoS Comput. Biol. Josh Merel, Donald M. Pianto, John P. Cunningham, Liam Paninski
2015	—	`conf`	High-dimensional neural spike train analysis with generalized count linear dynamical systems. NIPS Yuanjun Gao, Lars Buesing, Krishna V. Shenoy, John P. Cunningham
2015	J	`jnl`	Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. John P. Cunningham, Zoubin Ghahramani
2015	J	`jnl`	Neuroprosthetic decoder training as imitation learning. CoRR Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
2015	A	`conf`	Psychophysical Detection Testing with Bayesian Active Learning. UAI Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
2015	J	`jnl`	Scaling Multidimensional Inference for Structured Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. Elad Gilboa, Yunus Saatci, John P. Cunningham
2015	J	`jnl`	Sparse Estimation in a Correlated Probit Model. CoRR Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
2014	A*	`conf`	Bayesian Optimization with Inequality Constraints. ICML Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham
2014	—	`conf`	Clustered factor analysis of multineuronal spike data. NIPS Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
2014	—	`conf`	Fast Kernel Learning for Multidimensional Pattern Extrapolation. NIPS Andrew Gordon Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai
2013	J	`jnl`	GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes. CoRR Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham
2013	—	`conf`	Scaling Multidimensional Gaussian Processes using Projected Additive Approximations. ICML (1) Elad Gilboa, Yunus Saatçi, John P. Cunningham
2012	—	`conf`	A brain machine interface control algorithm designed from a feedback control perspective. EMBC Vikash Gilja, Paul Nuyujukian, Cindy A. Chestek, John P. Cunningham, Byron M. Yu, Joline M. Fan, Stephen I. Ryu, Krishna V. Shenoy
2012	J	`jnl`	An L 1-regularized logistic model for detecting short-term neuronal interactions. J. Comput. Neurosci. 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
2012	A	`conf`	Gaussian Processes for time-marked time-series data. AISTATS John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen
2011	—	`conf`	Dynamical segmentation of single trials from population neural data. NIPS Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
2011	—	`conf`	Empirical models of spiking in neural populations. NIPS Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
2009	J	`jnl`	Influence of heart rate on the BOLD signal: The cardiac response function. NeuroImage Catie Chang, John P. Cunningham, Gary H. Glover
2009	J	`jnl`	Methods for estimating neural firing rates, and their application to brain-machine interfaces. Neural Networks John P. Cunningham, Vikash Gilja, Stephen I. Ryu, Krishna V. Shenoy
2009	A*	`conf`	Workshop summary: Numerical mathematics in machine learning. ICML Matthias W. Seeger, Suvrit Sra, John P. Cunningham
2008	A*	`conf`	Fast Gaussian process methods for point process intensity estimation. ICML John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
2008	—	`conf`	Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. NIPS Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
2007	—	`conf`	Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes. NIPS John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
2007	—	`conf`	Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models. ICONIP (1) Byron M. Yu, John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani
2006	—	`conf`	Increasing the Performance of Cortically-Controlled Prostheses. EMBC (Supplement) 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
2006	—	`conf`	Optimal Target Placement for Neural Communication Prostheses. EMBC John P. Cunningham, Byron M. Yu, Krishna V. Shenoy