Geoff Pleiss

According to our database, Geoff Pleiss authored at least 41 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Pathologies of Predictive Diversity in Deep Ensembles.

Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham

Trans. Mach. Learn. Res., 2024

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

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

CoRR, 2024

Online Continual Learning of Video Diffusion Models From a Single Video Stream.

Jason Yoo, Dylan Green, Geoff Pleiss, Frank Wood

CoRR, 2024

Approximation-Aware Bayesian Optimization.

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

CoRR, 2024

MCMC-driven learning.

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

CoRR, 2024

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

Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss

CoRR, 2024

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

Jinsoo Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss

Proceedings of the Forty-first International Conference on Machine Learning, 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, Alán Aspuru-Guzik, Geoff Pleiss

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Large-Scale Gaussian Processes via Alternating Projection.

Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Large-Scale Gaussian Processes via Alternating Projection.

Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner

CoRR, 2023

CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra.

Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sharp Calibrated Gaussian Processes.

Alexandre Capone, Sandra Hirche, Geoff Pleiss

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Convolutional Networks with Dense Connectivity.

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

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Variational Nearest Neighbor Gaussian Processes.

Luhuan Wu, Geoff Pleiss, John P. Cunningham

CoRR, 2022

Posterior and Computational Uncertainty in Gaussian Processes.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep Ensembles Work, But Are They Necessary?

Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, Richard S. Zemel, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Variational nearest neighbor Gaussian process.

Luhuan Wu, Geoff Pleiss, John P. Cunningham

Proceedings of the International Conference on Machine Learning, 2022

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization.

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

Proceedings of the International Conference on Machine Learning, 2022

2021

Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning.

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

CoRR, 2021

Scalable Cross Validation Losses for Gaussian Process Models.

Martin Jankowiak, Geoff Pleiss

CoRR, 2021

Hierarchical Inducing Point Gaussian Process for Inter-domain Observations.

Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham

CoRR, 2021

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

Geoff Pleiss, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Rectangular Flows for Manifold Learning.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bias-Free Scalable Gaussian Processes via Randomized Truncations.

Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham

Proceedings of the 38th International Conference on Machine Learning, 2021

Hierarchical Inducing Point Gaussian Process for Inter-domian Observations.

Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Deep Sigma Point Processes.

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

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

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Identifying Mislabeled Data using the Area Under the Margin Ranking.

Geoff Pleiss, Tianyi Zhang, Ethan R. Elenberg, Kilian Q. Weinberger

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Parametric Gaussian Process Regressors.

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

Proceedings of the 37th International Conference on Machine Learning, 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 E. Campbell, Kilian Q. Weinberger

Proceedings of the 8th International Conference on Learning Representations, 2020

Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning.

Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019

Sparse Gaussian Process Regression Beyond Variational Inference.

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

CoRR, 2019

Exact Gaussian Processes on a Million Data Points.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Constant-Time Predictive Distributions for Gaussian Processes.

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

Proceedings of the 35th International Conference on Machine Learning, 2018

Product Kernel Interpolation for Scalable Gaussian Processes.

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Memory-Efficient Implementation of DenseNets.

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

CoRR, 2017

On Fairness and Calibration.

Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

On Calibration of Modern Neural Networks.

Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger

Proceedings of the 34th International Conference on Machine Learning, 2017

Snapshot Ensembles: Train 1, Get M for Free.

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

Proceedings of the 5th International Conference on Learning Representations, 2017

Deep Feature Interpolation for Image Content Changes.

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

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017