Optimal Target Placement for Neural Communication Prostheses. - John P. Cunningham; Byron M. Yu; Krishna V. Shenoy (EMBC, 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; et al. (EMBC (Supplement), 2006)

Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models. - Byron M. Yu; John P. Cunningham; Krishna V. Shenoy; Maneesh Sahani (ICONIP (1), 2007)

Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes. - John P. Cunningham; Byron M. Yu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 2007)

Fast Gaussian process methods for point process intensity estimation. - John P. Cunningham; Krishna V. Shenoy; Maneesh Sahani (ICML, 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 (NIPS, 2008)

Workshop summary: Numerical mathematics in machine learning. - Matthias W. Seeger; Suvrit Sra; John P. Cunningham (ICML, 2009)

Influence of heart rate on the BOLD signal: The cardiac response function. - Catie Chang; John P. Cunningham; Gary H. Glover (NeuroImage, 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)

Empirical models of spiking in neural populations. - Jakob H. Macke; Lars Buesing; John P. Cunningham; Byron M. Yu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 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 (NIPS, 2011)

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 (EMBC, 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)

Gaussian Processes for time-marked time-series data. - John P. Cunningham; Zoubin Ghahramani; Carl Edward Rasmussen (AISTATS, 2012)

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations. - Elad Gilboa; Yunus Saatçi; John P. Cunningham (ICML (1), 2013)

GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes. - Andrew Gordon Wilson; Elad Gilboa; Arye Nehorai; John P. Cunningham (CoRR, 2013)

Bayesian Optimization with Inequality Constraints. - Jacob R. Gardner; Matt J. Kusner; Zhixiang Eddie Xu; Kilian Q. Weinberger; John P. Cunningham (ICML, 2014)

Clustered factor analysis of multineuronal spike data. - Lars Buesing; Timothy A. Machado; John P. Cunningham; Liam Paninski (NIPS, 2014)

Fast Kernel Learning for Multidimensional Pattern Extrapolation. - Andrew Gordon Wilson; Elad Gilboa; John P. Cunningham; Arye Nehorai (NIPS, 2014)

High-dimensional neural spike train analysis with generalized count linear dynamical systems. - Yuanjun Gao; Lars Buesing; Krishna V. Shenoy; John P. Cunningham (NIPS, 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 (NIPS, 2015)

Psychophysical Detection Testing with Bayesian Active Learning. - Jacob R. Gardner; Xinyu Song; Kilian Q. Weinberger; Dennis L. Barbour; John P. Cunningham (UAI, 2015)

Sparse Estimation in a Correlated Probit Model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (CoRR, 2015)

Linear dimensionality reduction: survey, insights, and generalizations. - John P. Cunningham; Zoubin Ghahramani (J. Mach. Learn. Res., 2015)

Scaling Multidimensional Inference for Structured Gaussian Processes. - Elad Gilboa; Yunus Saatci; John P. Cunningham (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Encoder-Decoder Optimization for Brain-Computer Interfaces. - Josh Merel; Donald M. Pianto; John P. Cunningham; Liam Paninski (PLoS Comput. Biol., 2015)

Slice Sampling on Hamiltonian Trajectories. - Benjamin Bloem-Reddy; John P. Cunningham (ICML, 2016)

Preconditioning Kernel Matrices. - Kurt Cutajar; Michael A. Osborne; John P. Cunningham; Maurizio Filippone (ICML, 2016)

Linear dynamical neural population models through nonlinear embeddings. - Yuanjun Gao; Evan W. Archer; Liam Paninski; John P. Cunningham (NIPS, 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 (NIPS, 2016)

Elliptical Slice Sampling with Expectation Propagation. - Francois Fagan; Jalaj Bhandari; John P. Cunningham (UAI, 2016)

Bayesian Learning of Kernel Embeddings. - Seth R. Flaxman; Dino Sejdinovic; John P. Cunningham; Sarah Filippi (UAI, 2016)

Neuroprosthetic Decoder Training as Imitation Learning. - Josh Merel; David E. Carlson; Liam Paninski; John P. Cunningham (PLoS Comput. Biol., 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)

Annular Augmentation Sampling. - Francois Fagan; Jalaj Bhandari; John P. Cunningham (AISTATS, 2017)

Maximum Entropy Flow Networks. - Gabriel Loaiza-Ganem; Yuanjun Gao; John P. Cunningham (ICLR (Poster), 2017)

Sparse probit linear mixed model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (Mach. Learn., 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)

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. - Scott W. Linderman; Gonzalo E. Mena; Hal James Cooper; Liam Paninski; John P. Cunningham (AISTATS, 2018)

Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity. - Andrea Insabato; John P. Cunningham; Matthieu Gilson (CoRR, 2018)

A Novel Variational Family for Hidden Nonlinear Markov Models. - Daniel Hernández 010; Antonio Khalil Moretti; Ziqiang Wei; Shreya Saxena; John P. Cunningham; Liam Paninski (CoRR, 2018)

A Probabilistic Model of Cardiac Physiology and Electrocardiograms. - Andrew C. Miller; Ziad Obermeyer; David M. Blei; John P. Cunningham; Sendhil Mullainathan (CoRR, 2018)

Calibrating Deep Convolutional Gaussian Processes. - Gia-Lac Tran; Edwin V. Bonilla; John P. Cunningham; Pietro Michiardi; Maurizio Filippone (AISTATS, 2019)

Deep Random Splines for Point Process Intensity Estimation. - Gabriel Loaiza-Ganem; John P. Cunningham (DGS@ICLR, 2019)

Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. - Andrew C. Miller; Ziad Obermeyer; John P. Cunningham; Sendhil Mullainathan (ICML, 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; et al. (NeurIPS, 2019)

Paraphrase Generation with Latent Bag of Words. - Yao Fu; Yansong Feng 002; John P. Cunningham (NeurIPS, 2019)

The continuous Bernoulli: fixing a pervasive error in variational autoencoders. - 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 M. Churchland; John P. Cunningham (NeurIPS, 2019)

Approximating exponential family models (not single distributions) with a two-network architecture. - Sean R. Bittner; John P. Cunningham (CoRR, 2019)

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 (ICBINB@NeurIPS, 2020)

The continuous categorical: a novel simplex-valued exponential family. - Elliott Gordon-Rodríguez; Gabriel Loaiza-Ganem; John P. Cunningham (ICML, 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 (NeurIPS, 2020)

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. - Andres Potapczynski; Gabriel Loaiza-Ganem; 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 R. Whiteway; Michael Schartner; Guido Meijer; Jean-Paul Noel; Erica Rodriguez; Claire Everett; Amy Norovich; Evan Schaffer; Neeli Mishra; C. Daniel Salzman; et al. (NeurIPS, 2020)

General linear-time inference for Gaussian Processes on one dimension. - Jackson Loper; David M. Blei; John P. Cunningham; Liam Paninski (CoRR, 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)

Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. - Luhuan Wu; Andrew Miller; Lauren Anderson; Geoff Pleiss; David M. Blei; John P. Cunningham (AISTATS, 2021)

Bias-Free Scalable Gaussian Processes via Randomized Truncations. - Andres Potapczynski; Luhuan Wu; Dan Biderman; Geoff Pleiss; John P. Cunningham (ICML, 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, 2021)

Posterior Collapse and Latent Variable Non-identifiability. - Yixin Wang 002; David M. Blei; John P. Cunningham (NeurIPS, 2021)

Learning sparse log-ratios for high-throughput sequencing data. - Elliott Gordon-Rodríguez; Thomas P. Quinn; John P. Cunningham (Bioinform., 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; et al. (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)

Medical Imaging and Machine Learning. - Rohan Shad; John P. Cunningham; Euan A. Ashley; Curtis P. Langlotz; William Hiesinger (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)

Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. - Jonathan Wenger; Geoff Pleiss; Philipp Hennig; John P. Cunningham; Jacob R. Gardner (CoRR, 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)

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)

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; et al. (PLoS Comput. Biol., 2021)

Denoising Deep Generative Models. - Gabriel Loaiza-Ganem; Brendan Leigh Ross; Luhuan Wu; John P. Cunningham; Jesse C. Cresswell; Anthony L. Caterini (ICBINB, 2022)

Scaling Structured Inference with Randomization. - Yao Fu; John P. Cunningham; Mirella Lapata (ICML, 2022)

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. - Jonathan Wenger; Geoff Pleiss; Philipp Hennig; John P. Cunningham; Jacob R. Gardner (ICML, 2022)

Variational nearest neighbor Gaussian process. - Luhuan Wu; Geoff Pleiss; John P. Cunningham (ICML, 2022)

Deep Ensembles Work, But Are They Necessary? - Taiga Abe; Estefany Kelly Buchanan; Geoff Pleiss; Richard S. Zemel; John P. Cunningham (NeurIPS, 2022)

Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. - Elliott Gordon-Rodríguez; Thomas P. Quinn; John P. Cunningham (NeurIPS, 2022)

Posterior and Computational Uncertainty in Gaussian Processes. - Jonathan Wenger; Geoff Pleiss; Marvin Pförtner; Philipp Hennig; John P. Cunningham (NeurIPS, 2022)

Variational Nearest Neighbor Gaussian Processes. - Luhuan Wu; Geoff Pleiss; John P. Cunningham (CoRR, 2022)

On the Normalizing Constant of the Continuous Categorical Distribution. - Elliott Gordon-Rodríguez; Gabriel Loaiza-Ganem; Andres Potapczynski; John P. Cunningham (CoRR, 2022)

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. - Luhuan Wu; Brian L. Trippe; Christian A. Naesseth; David M. Blei; John P. Cunningham (NeurIPS, 2023)

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 (NeurIPS, 2024)

Approximation-Aware Bayesian Optimization. - Natalie Maus; Kyurae Kim; David Eriksson; Geoff Pleiss; 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)

Theoretical Limitations of Ensembles in the Age of Overparameterization. - Niclas Dern; John P. Cunningham; Geoff Pleiss (CoRR, 2024)

Pathologies of Predictive Diversity in Deep Ensembles. - Taiga Abe; Estefany Kelly Buchanan; Geoff Pleiss; John P. Cunningham (Trans. Mach. Learn. Res., 2024)

LoRA Learns Less and Forgets Less. - Dan Biderman; Jacob P. Portes; Jose Javier Gonzalez Ortiz; Mansheej Paul; Philip Greengard; Connor Jennings; Daniel King; Sam Havens; Vitaliy Chiley; Jonathan Frankle; Cody Blakeney; John Patrick Cunningham (Trans. Mach. Learn. Res., 2024)

Scalable Differentially Private Bayesian Optimization. - Getoar Sopa; Juraj Marusic; Marco Avella-Medina; John P. Cunningham (CoRR, 2025)

Variational Deep Learning via Implicit Regularization. - Jonathan Wenger; Beau Coker; Juraj Marusic; John P. Cunningham (CoRR, 2025)

Bayesian Invariance Modeling of Multi-Environment Data. - Luhuan Wu; Mingzhang Yin; Yixin Wang 002; John P. Cunningham; David M. Blei (CoRR, 2025)