Deep Feature Interpolation for Image Content Changes. - Paul Upchurch; Jacob R. Gardner; Geoff Pleiss; Robert Pless; Noah Snavely; Kavita Bala; Kilian Q. Weinberger (CVPR, 2017)

Snapshot Ensembles: Train 1, Get M for Free. - Gao Huang 001; Yixuan Li 001; Geoff Pleiss; Zhuang Liu 003; John E. Hopcroft; Kilian Q. Weinberger (ICLR (Poster), 2017)

On Calibration of Modern Neural Networks. - Chuan Guo 001; Geoff Pleiss; Yu Sun 020; Kilian Q. Weinberger (ICML, 2017)

On Fairness and Calibration. - Geoff Pleiss; Manish Raghavan; Felix Wu; Jon M. Kleinberg; Kilian Q. Weinberger (NIPS, 2017)

Memory-Efficient Implementation of DenseNets. - Geoff Pleiss; Danlu Chen; Gao Huang 001; Tongcheng Li; Laurens van der Maaten; Kilian Q. Weinberger (CoRR, 2017)

Product Kernel Interpolation for Scalable Gaussian Processes. - Jacob R. Gardner; Geoff Pleiss; Ruihan Wu; Kilian Q. Weinberger; Andrew Gordon Wilson (AISTATS, 2018)

Constant-Time Predictive Distributions for Gaussian Processes. - Geoff Pleiss; Jacob R. Gardner; Kilian Q. Weinberger; Andrew Gordon Wilson (ICML, 2018)

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. - Jacob R. Gardner; Geoff Pleiss; Kilian Q. Weinberger; David Bindel; Andrew Gordon Wilson (NeurIPS, 2018)

Exact Gaussian Processes on a Million Data Points. - Ke Alexander Wang; Geoff Pleiss; Jacob R. Gardner; Stephen Tyree; Kilian Q. Weinberger; Andrew Gordon Wilson (NeurIPS, 2019)

Sparse Gaussian Process Regression Beyond Variational Inference. - Martin Jankowiak; Geoff Pleiss; Jacob R. Gardner (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)

Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. - Yurong You; Yan Wang 051; Wei-Lun Chao; Divyansh Garg; Geoff Pleiss; Bharath Hariharan; Mark E. Campbell; Kilian Q. Weinberger (ICLR, 2020)

Parametric Gaussian Process Regressors. - Martin Jankowiak; Geoff Pleiss; Jacob R. Gardner (ICML, 2020)

Identifying Mislabeled Data using the Area Under the Margin Ranking. - Geoff Pleiss; Tianyi Zhang 007; Ethan R. Elenberg; Kilian Q. Weinberger (NeurIPS, 2020)

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. - Geoff Pleiss; Martin Jankowiak; David Eriksson; Anil Damle; Jacob R. Gardner (NeurIPS, 2020)

Deep Sigma Point Processes. - Martin Jankowiak; Geoff Pleiss; Jacob R. Gardner (UAI, 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)

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)

Scalable Cross Validation Losses for Gaussian Process Models. - Martin Jankowiak; Geoff Pleiss (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)

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)

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)

Convolutional Networks with Dense Connectivity. - Gao Huang 001; Zhuang Liu 003; Geoff Pleiss; Laurens van der Maaten; Kilian Q. Weinberger (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Sharp Calibrated Gaussian Processes. - Alexandre Capone; Sandra Hirche; Geoff Pleiss (NeurIPS, 2023)

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. - Andres Potapczynski; Marc Finzi; Geoff Pleiss; Andrew Gordon Wilson (NeurIPS, 2023)

Large-Scale Gaussian Processes via Alternating Projection. - Kaiwen Wu; Jonathan Wenger; Haydn Thomas Jones; Geoff Pleiss; Jacob R. Gardner (AISTATS, 2024)

A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? - Agustinus Kristiadi; Felix Strieth-Kalthoff; Marta Skreta; Pascal Poupart; Alán Aspuru-Guzik; Geoff Pleiss (ICML, 2024)

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. - Jinsoo Yoo; Yunpeng Liu 007; Frank Wood; Geoff Pleiss (ICML, 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)

Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. - Jason Yoo; Yunpeng Liu 007; Frank Wood; Geoff Pleiss (CoRR, 2024)

MCMC-driven learning. - Alexandre Bouchard-Côté; Trevor Campbell; Geoff Pleiss; Nikola Surjanovic (CoRR, 2024)

Online Continual Learning of Video Diffusion Models From a Single Video Stream. - Jason Yoo; Dylan Green; Geoff Pleiss; Frank Wood (CoRR, 2024)

How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? - Agustinus Kristiadi; Felix Strieth-Kalthoff; Sriram Ganapathi Subramanian; Vincent Fortuin; Pascal Poupart; Geoff Pleiss (CoRR, 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)

Asymmetric Duos: Sidekicks Improve Uncertainty. - Tim G. Zhou; Evan Shelhamer; Geoff Pleiss (CoRR, 2025)