Pleiss, Geoff

36 publications

NeurIPS 2025 Asymmetric Duos: Sidekicks Improve Uncertainty Tim G. Zhou, Evan Shelhamer, Geoff Pleiss

ICML 2025 Theoretical Limitations of Ensembles in the Age of Overparameterization Niclas Dern, John Patrick Cunningham, Geoff Pleiss

ICLRW 2025 What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization Tristan Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim G. J. Rudner, Vincent Fortuin, Agustinus Kristiadi

ICML 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, Alan Aspuru-Guzik, Geoff Pleiss

NeurIPS 2024 Approximation-Aware Bayesian Optimization Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, 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

AISTATS 2024 Large-Scale Gaussian Processes via Alternating Projection Kaiwen Wu, Jonathan Wenger, Haydn T Jones, Geoff Pleiss, Jacob Gardner

ICML 2024 Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss

TMLR 2024 Pathologies of Predictive Diversity in Deep Ensembles Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, John Patrick Cunningham

NeurIPS 2023 CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew G Wilson

NeurIPS 2023 Sharp Calibrated Gaussian Processes Alexandre Capone, Sandra Hirche, Geoff Pleiss

NeurIPSW 2023 The Effects of Ensembling on Long-Tailed Data E. Kelly Buchanan, Geoff Pleiss, John Patrick Cunningham

NeurIPS 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

ICML 2022 Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner

NeurIPSW 2022 The Best Deep Ensembles Sacrifice Predictive Diversity Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, John Patrick Cunningham

ICML 2022 Variational Nearest Neighbor Gaussian Process Luhuan Wu, Geoff Pleiss, John P Cunningham

AISTATS 2021 Hierarchical Inducing Point Gaussian Process for Inter-Domian Observations Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David Blei, John Cunningham

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

NeurIPS 2021 Rectangular Flows for Manifold Learning Anthony L Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham

ICMLW 2021 Rectangular Flows for Manifold Learning Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John Patrick Cunningham

NeurIPS 2021 The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss, John P. Cunningham

UAI 2020 Deep Sigma Point Processes Martin Jankowiak, Geoff Pleiss, Jacob Gardner

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

NeurIPS 2020 Identifying Mislabeled Data Using the Area Under the Margin Ranking Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, Kilian Q. Weinberger

ICML 2020 Parametric Gaussian Process Regressors Martin Jankowiak, Geoff Pleiss, Jacob Gardner

ICLR 2020 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

NeurIPSW 2020 Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning Elliott Gordon-Rodriguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John Patrick Cunningham

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

ICML 2018 Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss, Jacob Gardner, Kilian Weinberger, Andrew Gordon Wilson

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

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

CVPR 2017 Deep Feature Interpolation for Image Content Changes Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Weinberger

ICML 2017 On Calibration of Modern Neural Networks Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger

NeurIPS 2017 On Fairness and Calibration Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, Kilian Q. Weinberger

ICLR 2017 Snapshot Ensembles: Train 1, Get M for Free Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger