You need to enable JavaScript to run this app.
Geoff Pleiss
Research
Overview
Uncertainty Quantification
Overparameterization and Ensemble Methods
Numerical Methods for Gaussian Processes
Approximate Inference for Gaussian Processes
"Reliable" Deep Learning
Probabilistic Modeling
Scientific Applications
Teaching
STAT 520P (Bayesian Optimization)
STAT 548 (PhD Qualifying Course)
Open Source
GPyTorch
LinearOperator
Blog
Neural Network Calibration
Area Under the Margin (AUM)
CV
Bio
Google Scholar
Github
Complete List of Publications by Topic
Uncertainty Quantification
NEW
Sharp Calibrated Gaussian Processes
Alexandre Capone
Sandra Hirche
Geoff Pleiss
— In NeurIPS, 2023
PDF
Posterior and Computational Uncertainty in Gaussian Processes
Jonathan Wenger
Geoff Pleiss
Marvin Pförtner
Philipp Hennig
John P. Cunningham
— In NeurIPS, 2022
PDF
Github
Deep Ensembles Work, But Are They Necessary?
Taiga Abe*
E. Kelly Buchanan*
Geoff Pleiss
Richard Zemel
John P. Cunningham
* Authors contributed equally
— In NeurIPS, 2022
PDF
Github
On Fairness and Calibration
Geoff Pleiss*
Manish Raghavan*
Felix Wu
Jon Kleinberg
Kilian Q. Weinberger
* Authors contributed equally
— In NeurIPS, 2017
PDF
Github
On Calibration of Modern Neural Networks
Chuan Gao*
Geoff Pleiss*
Yu Sun*
Kilian Q. Weinberger
* Authors contributed equally
— In ICML, 2017
PDF
Github
Talk
Overparameterization and Ensemble Methods
NEW
Pathologies of Predictive Diversity in Deep Ensembles
Taiga Abe
E. Kelly Buchanan
Geoff Pleiss
John P. Cunningham
— Under Submission
PDF
The Best Deep Ensembles Sacrifice Predictive Diversity
[Most Surprising Result Award]
Taiga Abe*
E. Kelly Buchanan*
Geoff Pleiss
John P. Cunningham
* Authors contributed equally
— In NeurIPS "I Can't Believe It's Not Better!" Workshop, 2022
PDF
Deep Ensembles Work, But Are They Necessary?
Taiga Abe*
E. Kelly Buchanan*
Geoff Pleiss
Richard Zemel
John P. Cunningham
* Authors contributed equally
— In NeurIPS, 2022
PDF
Github
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Geoff Pleiss
John P. Cunningham
— In NeurIPS, 2021
PDF
Github
Talk
Snapshot Ensembles: Train 1, get M for free
Gao Huang*
Yixuan Li*
Geoff Pleiss
Zhuang Liu
John Hopcroft
Kilian Q. Weinberger
* Authors contributed equally
— In ICLR, 2017
PDF
Github
Numerical Methods for Gaussian Processes and Machine Learning
NEW
Large-Scale Gaussian Processes via Alternating Projection
Kaiwen Wu
Jonathan Wenger
Hadyn Jones
Geoff Pleiss
Jacob R. Gardner
— Under Submission
PDF
NEW
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra
Andres Potapczynski*
Marc Anton Finzi*
Geoff Pleiss
Andrew Gordon Wilson
* Authors contributed equally
— In NeurIPS, 2023
PDF
Github
Posterior and Computational Uncertainty in Gaussian Processes
Jonathan Wenger
Geoff Pleiss
Marvin Pförtner
Philipp Hennig
John P. Cunningham
— In NeurIPS, 2022
PDF
Github
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
[long oral]
Jonathan Wenger
Geoff Pleiss
Philipp Hennig
John P. Cunningham
Jacob R. Gardner
— In ICML, 2022
PDF
Github
Talk
Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski*
Luhuan Wu*
Dan Biderman*
Geoff Pleiss
John P. Cunningham
* Authors contributed equally
— In ICML, 2021
PDF
Github
Talk
A Scalable and Flexible Framework for Gaussian Processes via Matrix-Vector Multiplication
Geoff Pleiss
Ph.D. Thesis, 2020
PDF
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss
Martin Jankowiak
David Eriksson
Anil Damle
Jacob R. Gardner
— In NeurIPS, 2020
PDF
Github
Talk
Exact Gaussian Processes on a Million Data Points
Ke Alexander Wang*
Geoff Pleiss*
Jacob R. Gardner
Stephen Tyree
Kilian Q. Weinberger
Andrew Gordon Wilson
* Authors contributed equally
— In NeurIPS, 2019
PDF
Github
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
[spotlight]
Jacob R. Gardner*
Geoff Pleiss*
David Bindel
Kilian Q. Weinberger
Andrew Gordon Wilson
* Authors contributed equally
— In NeurIPS, 2018
PDF
Github
Talk
Constant-Time Predictive Distributions for Gaussian Processes
Geoff Pleiss
Jacob R. Gardner
Kilian Q. Weinberger
Andrew Gordon Wilson
— In ICML, 2018
PDF
Github
Product Kernel Interpolation for Scalable Gaussian Processes
Jacob R. Gardner
Geoff Pleiss
Ruihan Wu
Kilian Q. Weinberger
Andrew Gordon Wilson
— In AISTATS, 2018
PDF
Github
Approximate Inference for Gaussian Processes
Variational Nearest Neighbor Gaussian Processes
Luhuan Wu
Geoff Pleiss
John P. Cunningham
— In ICML, 2022
PDF
Github
Talk
Scalable Cross Validation Losses for Gaussian Process Models
Martin Jankowiak
Geoff Pleiss
—
Tech Report
, 2021
PDF
Parametric Gaussian Process Regressors
Martin Jankowiak
Geoff Pleiss
Jacob R. Gardner
— In ICML, 2020
PDF
Github
Talk
"Reliable" Deep Learning
Identifying Mislabeled Data using the Area Under the Margin Ranking
Geoff Pleiss
Tianyi Zhang
Ethan R. Elenberg
Kilian Q. Weinberger
— In NeurIPS, 2020
PDF
Github
Talk
Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving.
Yurong You*
Yan Wang*
Wei-Lun Chao*
Divyansh Garg
Geoff Pleiss
Bharath Hariharan
Mark Campbell
Kilian Q. Weinberger
* Authors contributed equally
— In ICLR, 2020
PDF
Github
Convolutional Networks with Dense Connectivity
Gao Huang*
Zhuang Liu*
Geoff Pleiss
Laurens van der Maaten
Kilian Q. Weinberger
* Authors contributed equally
— In Pattern Analysis and Machine Intelligence, 2019
PDF
Github
Deep Feature Interpolation for Image Content Changes
Paul Upchurch*
Jacob R. Gardner*
Geoff Pleiss
Robert Pless
Noah Snavely
Kavita Bala
Kilian Q. Weinberger
* Authors contributed equally
— In CVPR, 2017
PDF
Github
Probabilistic Modeling
Rectangular Flows for Manifold Learning
Anthony L. Caterini*
Gabriel Loaiza-Ganem*
Geoff Pleiss
John P. Cunningham
* Authors contributed equally
— In NeurIPS, 2021
PDF
Github
Talk
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Geoff Pleiss
John P. Cunningham
— In NeurIPS, 2021
PDF
Github
Talk
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Elliott Gordon-Rodriguez
Gabriel Loaiza-Ganem
Geoff Pleiss
John P. Cunningham
— In NeurIPS "I Can't Believe It's Not Better!" Workshop, 2020
PDF
Github
Talk
Deep Sigma Point Processes
Martin Jankowiak
Geoff Pleiss
Jacob R. Gardner
— In UAI, 2020
PDF
Github
Scientific Applications
Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data From Modern X-Ray Diffraction
Jordan Venderley
Michael Matty
Krishnanand Mallayya
Matthew Krogstad
Jacob Ruff
Geoff Pleiss
Varsha Kishore
David Mandrus
Daniel Phelan
Lekhanath Poudel
Andrew Gordon Wilson
Kilian Q. Weinberger
Puspa Upreti
Michael R. Norman
Stephan Rosenkranz
Ray Osborn
Eun-Ah Kim
— In Proceedings of the National Academy of Sciences, 2022
PDF
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
Luhuan Wu*
Andrew Miller*
Lauren Anderson
Geoff Pleiss
David Blei
John P. Cunningham
* Authors contributed equally
— In AISTATS, 2021
PDF
Github
Potential Predictability of Regional Precipitation and Discharge Extremes Using Synoptic-Scale Climate Information via Machine Learning
James Knighton
Geoff Pleiss
Elizabeth Carter
Steven Lyon
M Todd Walter
Scott Steinschneider
— In Journal of Hydrometeorology, 2019
PDF