Geoff Pleiss

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 61

Top Topics

Gaussian Process

Top Venues

Publications

Jason Yoo, Dylan Green, Geoff Pleiss, Frank Wood
Online Continual Learning of Video Diffusion Models From a Single Video Stream. CoRR (2024)
Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? CoRR (2024)
Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner
Large-Scale Gaussian Processes via Alternating Projection. AISTATS (2024)
Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic
MCMC-driven learning. CoRR (2024)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
Pathologies of Predictive Diversity in Deep Ensembles. Trans. Mach. Learn. Res. 2024 (2024)
Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
Approximation-Aware Bayesian Optimization. CoRR (2024)
Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning. CoRR (2024)
Agustinus Kristiadi, Felix Strieth-Kalthoff, Sriram Ganapathi Subramanian, Vincent Fortuin, Pascal Poupart, Geoff Pleiss
How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization? CoRR (2024)
Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. NeurIPS (2023)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham
Pathologies of Predictive Diversity in Deep Ensembles. CoRR (2023)
Alexandre Capone, Geoff Pleiss, Sandra Hirche
Sharp Calibrated Gaussian Processes. CoRR (2023)
Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson
CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra. CoRR (2023)
Alexandre Capone, Sandra Hirche, Geoff Pleiss
Sharp Calibrated Gaussian Processes. NeurIPS (2023)
Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner
Large-Scale Gaussian Processes via Alternating Projection. CoRR (2023)
Luhuan Wu, Geoff Pleiss, John P. Cunningham
Variational nearest neighbor Gaussian process. ICML (2022)
Luhuan Wu, Geoff Pleiss, John P. Cunningham
Variational Nearest Neighbor Gaussian Processes. CoRR (2022)
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
Posterior and Computational Uncertainty in Gaussian Processes. CoRR (2022)
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS (2022)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML (2022)
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
Convolutional Networks with Dense Connectivity. IEEE Trans. Pattern Anal. Mach. Intell. 44 (12) (2022)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
Deep Ensembles Work, But Are They Necessary? NeurIPS (2022)
Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham
Deep Ensembles Work, But Are They Necessary? CoRR (2022)
Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR (2021)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR (2021)
Martin Jankowiak, Geoff Pleiss
Scalable Cross Validation Losses for Gaussian Process Models. CoRR (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. CoRR (2021)
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
Bias-Free Scalable Gaussian Processes via Randomized Truncations. CoRR (2021)
Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. NeurIPS (2021)
Geoff Pleiss, John P. Cunningham
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. CoRR (2021)
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
Bias-Free Scalable Gaussian Processes via Randomized Truncations. ICML (2021)
Geoff Pleiss, John P. Cunningham
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective. NeurIPS (2021)
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger
Identifying Mislabeled Data using the Area Under the Margin Ranking. CoRR (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. CoRR (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. ICBINB@NeurIPS (2020)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Deep Sigma Point Processes. UAI (2020)
Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger
Identifying Mislabeled Data using the Area Under the Margin Ranking. NeurIPS (2020)
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR (2020)
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
Convolutional Networks with Dense Connectivity. CoRR (2020)
Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark E. Campbell, Kilian Q. Weinberger
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. ICLR (2020)
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. NeurIPS (2020)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Deep Sigma Point Processes. CoRR (2020)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Parametric Gaussian Process Regressors. ICML (2020)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Sparse Gaussian Process Regression Beyond Variational Inference. CoRR (2019)
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Exact Gaussian Processes on a Million Data Points. NeurIPS (2019)
Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. CoRR (2019)
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Exact Gaussian Processes on a Million Data Points. CoRR (2019)
Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
Constant-Time Predictive Distributions for Gaussian Processes. ICML (2018)
Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. CoRR (2018)
Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Gordon Wilson
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. NeurIPS (2018)
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
Product Kernel Interpolation for Scalable Gaussian Processes. CoRR (2018)
Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
Constant-Time Predictive Distributions for Gaussian Processes. CoRR (2018)
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
Product Kernel Interpolation for Scalable Gaussian Processes. AISTATS (2018)
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger
Snapshot Ensembles: Train 1, Get M for Free. ICLR (Poster) (2017)
Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger
Memory-Efficient Implementation of DenseNets. CoRR (2017)
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger
On Fairness and Calibration. CoRR (2017)
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger
On Fairness and Calibration. NIPS (2017)
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger
Snapshot Ensembles: Train 1, get M for free. CoRR (2017)
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
On Calibration of Modern Neural Networks. CoRR (2017)
Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
On Calibration of Modern Neural Networks. ICML (2017)
Paul Upchurch, Jacob R. Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Q. Weinberger
Deep Feature Interpolation for Image Content Changes. CVPR (2017)