Complete List of Publications By Topic

Uncertainty Quantification

• NEWAsymmetric Duos: Sidekicks Improve Uncertainty

  • Tim G. Zhou
  • Evan Shelhamer
  • Geoff Pleiss

  — Under Submission
  • PDF

• NEWTheoretical Limitations of Ensembles in the Age of Overparameterization[Spotlight presentation]

  • Niclas Dern
  • John P. Cunningham
  • Geoff Pleiss

  — In International Conference on Machine Learning, 2025
  • PDF
  • Github
  • Talk

• Sharp Calibrated Gaussian Processes

  • Alexandre Capone
  • Sandra Hirche
  • Geoff Pleiss

  — In Neural Information Processing Systems, 2023
  • PDF

• Posterior and Computational Uncertainty in Gaussian Processes

  • Jonathan Wenger
  • Geoff Pleiss
  • Marvin Pförtner
  • Philipp Hennig
  • John P. Cunningham

  — In Neural Information Processing Systems, 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 Neural Information Processing Systems, 2022
  • PDF
  • Github
  • Talk

• On Fairness and Calibration

  • Geoff Pleiss*
  • Manish Raghavan*
  • Felix Wu
  • Jon Kleinberg
  • Kilian Q. Weinberger

  * Authors contributed equally

  — In Neural Information Processing Systems, 2017
  • PDF
  • Github

• On Calibration of Modern Neural Networks

  • Chuan Guo*
  • Geoff Pleiss*
  • Yu Sun*
  • Kilian Q. Weinberger

  * Authors contributed equally

  — In International Conference on Machine Learning, 2017
  • PDF
  • Github
  • Talk

Bayesian Optimization

• NEWWhat Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization

  • Tristan Cinquin
  • Stanley Lo
  • Felix Strieth-Kalthoff
  • Alán Aspuru-Guzik
  • Geoff Pleiss
  • Robert Balmer
  • Tim G. J. Rudner
  • Vincent Fortuin
  • Agustinus Kristiadi

  — In Symposium on Advances in Approximate Bayesian Inference, Workshop Track, 2025

• Approximation-Aware Bayesian Optimization[Spotlight presentation]

  • Natalie Maus
  • Kyurae Kim
  • Geoff Pleiss
  • David Eriksson
  • John P. Cunningham
  • Jacob R. Gardner

  — In Neural Information Processing Systems, 2024
  • PDF
  • Github

• How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?

  • Agustinus Kristiadi
  • Felix Strieth-Kalthoff
  • Sriram Ganapathi Subramanian
  • Vincent Fortuin
  • Pascal Poupart
  • Geoff Pleiss

  — In Symposium on Advances in Approximate Bayesian Inference, Workshop Track, 2024
  • PDF
  • Github

• 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

  — In International Conference on Machine Learning, 2024
  • PDF
  • Github

• Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

  • Geoff Pleiss
  • Martin Jankowiak
  • David Eriksson
  • Anil Damle
  • Jacob R. Gardner

  — In Neural Information Processing Systems, 2020
  • PDF
  • Github
  • Talk

Deep Learning

• NEWAsymmetric Duos: Sidekicks Improve Uncertainty

  • Tim G. Zhou
  • Evan Shelhamer
  • Geoff Pleiss

  — Under Submission
  • PDF

• NEWTheoretical Limitations of Ensembles in the Age of Overparameterization[Spotlight presentation]

  • Niclas Dern
  • John P. Cunningham
  • Geoff Pleiss

  — In International Conference on Machine Learning, 2025
  • PDF
  • Github
  • Talk

• Pathologies of Predictive Diversity in Deep Ensembles[Featured Paper]

  • Taiga Abe
  • E. Kelly Buchanan
  • Geoff Pleiss
  • John P. Cunningham

  — In Transactions on Machine Learning Research, 2024
  • PDF
  • Talk

• The Effects of Ensembling on Long-Tailed Data

  • E. Kelly Buchanan
  • Geoff Pleiss
  • Yixin Wang
  • John P. Cunningham

  — In NeurIPS "Heavy Tails in ML: Structure, Stability, Dynamics" Workshop, 2023
  • PDF
  • Github

• The Best Deep Ensembles Sacrifice Predictive Diversity[Oral Presentation]

  • 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 Neural Information Processing Systems, 2022
  • PDF
  • Github
  • Talk

• The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective

  • Geoff Pleiss
  • John P. Cunningham

  — In Neural Information Processing Systems, 2021
  • PDF
  • Github
  • Talk

• Identifying Mislabeled Data using the Area Under the Margin Ranking

  • Geoff Pleiss
  • Tianyi Zhang
  • Ethan Elenberg
  • Kilian Q. Weinberger

  — In Neural Information Processing Systems, 2020
  • PDF
  • Github
  • Talk

• Convolutional Networks with Dense Connectivity

  • Gao Huang*
  • Zhuang Liu*
  • Geoff Pleiss
  • Laurens van der Maaten
  • Kilian Q. Weinberger

  * Authors contributed equally

  — In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
  • PDF
  • Github

• On Calibration of Modern Neural Networks

  • Chuan Guo*
  • Geoff Pleiss*
  • Yu Sun*
  • Kilian Q. Weinberger

  * Authors contributed equally

  — In International Conference on Machine Learning, 2017
  • PDF
  • Github
  • Talk

• Memory-Efficient Implementation of DenseNets

  • Geoff Pleiss*
  • Danlu Chen*
  • Gao Huang
  • Tongcheng Li
  • Laurens van der Maaten
  • Kilian Q. Weinberger

  * Authors contributed equally

  — In , 2017
  • PDF
  • Github

• Snapshot Ensembles: Train 1, Get M for Free

  • Gao Huang*
  • Yixuan Li*
  • Geoff Pleiss
  • Zhuang Liu
  • John E. Hopcroft
  • Kilian Q. Weinberger

  * Authors contributed equally

  — In International Conference on Learning Representations, 2017
  • PDF
  • Github

Scalable Gaussian Processes via Numerical Methods

• Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference

  • Jonathan Wenger
  • Kaiwen Wu
  • Philipp Hennig
  • Jacob R. Gardner
  • Geoff Pleiss
  • John P. Cunningham

  — In Neural Information Processing Systems, 2024
  • PDF
  • Github

• Large-Scale Gaussian Processes via Alternating Projection

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

  — In Artificial Intelligence and Statistics, 2024
  • PDF
  • Github

• 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 Neural Information Processing Systems, 2023
  • PDF
  • Github

• Posterior and Computational Uncertainty in Gaussian Processes

  • Jonathan Wenger
  • Geoff Pleiss
  • Marvin Pförtner
  • Philipp Hennig
  • John P. Cunningham

  — In Neural Information Processing Systems, 2022
  • PDF
  • Github

• Preconditioning for Scalable Gaussian Process Hyperparameter Optimization[Oral Presentation]

  • Jonathan Wenger
  • Geoff Pleiss
  • Philipp Hennig
  • John P. Cunningham
  • Jacob R. Gardner

  — In International Conference on Machine Learning, 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 International Conference on Machine Learning, 2021
  • PDF
  • Github
  • Talk

• Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

  • Geoff Pleiss
  • Martin Jankowiak
  • David Eriksson
  • Anil Damle
  • Jacob R. Gardner

  — In Neural Information Processing Systems, 2020
  • PDF
  • Github
  • Talk

• Exact Gaussian processes on a million data points

  • Ke Wang*
  • Geoff Pleiss*
  • Jacob R. Gardner
  • Stephen Tyree
  • Kilian Q. Weinberger
  • Andrew Gordon Wilson

  * Authors contributed equally

  — In Neural Information Processing Systems, 2019
  • PDF
  • Github

• GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.[Spotlight presentation]

  • Jacob R. Gardner*
  • Geoff Pleiss*
  • David Bindel
  • Kilian Q. Weinberger
  • Andrew Gordon Wilson

  * Authors contributed equally

  — In Neural Information Processing Systems, 2018
  • PDF
  • Github
  • Talk

• Constant Time Predictive Distributions for Gaussian Processes

  • Geoff Pleiss
  • Jacob R. Gardner
  • Andrew Gordon Wilson
  • Kilian Q. Weinberger

  — In International Conference on Machine Learning, 2018
  • PDF
  • Github

• Product Kernel Interpolation for Scalable Gaussian Processes

  • Jacob R. Gardner
  • Geoff Pleiss
  • Ruihan Wu
  • Andrew Gordon Wilson
  • Kilian Q. Weinberger

  — In Artificial Intelligence and Statistics, 2018
  • PDF
  • Github

Spatiotemporal Modeling

• NEWA Nearby Dark Molecular Cloud in the Local Bubble Revealed via H_2 Fluorescence

  • Blakesley Burkhart
  • Thavisha E. Dharmawardena
  • Shmuel Bialy
  • Thomas J. Haworth
  • Fernando Cruz Aguirre
  • Young-Soo Jo
  • B.G. Andersson
  • Haeun Chung
  • Jerry Edelstein
  • Isabelle Grenier
  • Erika T. Hamden
  • Wonyong Han
  • Keri Hoadley
  • Min-Young Lee
  • Kyoung-Wook Min
  • Thomas Müller
  • Kate Pattle
  • J. E. G. Peek
  • Geoff Pleiss
  • David Schiminovich
  • Kwang-Il Seon
  • Andrew Gordon Wilson
  • Catherine Zucker

  — In Nature Astronomy, 2025
  • PDF

• Variational Nearest Neighbor Gaussian Processes

  • Luhuan Wu
  • Geoff Pleiss
  • John P. Cunningham

  — In International Conference on Machine Learning, 2022
  • PDF
  • Github
  • Talk

• Scalable Cross Validation Losses for Gaussian Process Models

  • Martin Jankowiak
  • Geoff Pleiss

  — In , 2021
  • 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 Artificial Intelligence and Statistics, 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

Probabilistic Modeling

• How Inductive Bias in Machine Learning Aligns with Optimality in Economic Dynamics

  • Mahdi Ebrahimi Kahou
  • James Yu
  • Jesse Perla
  • Geoff Pleiss

  — Under Submission
  • PDF

• MCMC-driven learning

  • Alexandre Bouchard-Côté
  • Trevor Campbell
  • Geoff Pleiss
  • Nikola Surjanovic

  — Under Submission
  • PDF

• 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

• The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective

  • Geoff Pleiss
  • John P. Cunningham

  — In Neural Information Processing Systems, 2021
  • PDF
  • Github
  • Talk

• Rectangular Flows for Manifold Learning

  • Anthony L. Caterini*
  • Gabriel Loaiza-Ganem*
  • Geoff Pleiss
  • John P. Cunningham

  * Authors contributed equally

  — In Neural Information Processing Systems, 2021
  • PDF
  • Github
  • Talk

• Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning[Oral Presentation]

  • 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

• Deep Sigma Point Processes

  • Martin Jankowiak
  • Geoff Pleiss
  • Jacob R. Gardner

  — In Uncertainty in Artificial Intelligence, 2020
  • PDF
  • Github

• Parametric Gaussian Process Regressors

  • Martin Jankowiak
  • Geoff Pleiss
  • Jacob R. Gardner

  — In International Conference on Machine Learning, 2020
  • PDF
  • Github
  • Talk

Computer Vision

• NEWLifelong Learning of Video Diffusion Models From a Single Video Stream

  • Jason Yoo
  • Yingchen He
  • Saeid Naderiparizi
  • Dylan Green
  • Gido M. van de Ven
  • Geoff Pleiss
  • Frank Wood

  — Under Submission
  • PDF

• Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning

  • Jinsoo Yoo
  • Yunpeng Liu
  • Frank Wood
  • Geoff Pleiss

  — In International Conference on Machine Learning, 2024
  • PDF
  • Github

• 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 International Conference on Learning Representations, 2020
  • PDF
  • Github

• Deep feature interpolation for image content changes

  • Paul Upchurch*
  • Jacob Gardner*
  • Geoff Pleiss
  • Kavita Bala
  • Robert Pless
  • Noah Snavely
  • Kilian Q. Weinberger

  * Authors contributed equally

  — In Computer Vision and Pattern Recognition, 2017
  • PDF
  • Github