Complete List of Publications by Topic

Uncertainty Quantification

• NEWSharp 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

• NEWPathologies 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

• NEWLarge-Scale Gaussian Processes via Alternating Projection

  • Kaiwen Wu
  • Jonathan Wenger
  • Hadyn Jones
  • Geoff Pleiss
  • Jacob R. Gardner

  — Under Submission
  • PDF

• NEWCoLA: 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