公開取用

On calibration of modern neural networks

C Guo, G Pleiss, Y Sun, KQ Weinberger

International Conference on Machine Learning, 1321-1330, 2017

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

Gpytorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration

JR Gardner, G Pleiss, KQ Weinberger, D Bindel, AG Wilson

Advances in Neural Information Processing Systems, 7576-7586, 2018

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

On fairness and calibration

G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger

Advances in Neural Information Processing Systems, 2017

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

[PDF] ieee.org

Convolutional Networks with Dense Connectivity

G Huang, Z Liu, G Pleiss, L Van Der Maaten, KQ Weinberger

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

Exact Gaussian processes on a million data points

KA Wang, G Pleiss, JR Gardner, S Tyree, KQ Weinberger, AG Wilson

Advances in Neural Information Processing Systems, 2019

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

Constant-time predictive distributions for Gaussian processes

G Pleiss, JR Gardner, KQ Weinberger, AG Wilson

International Conference on Machine Learning, 2018

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

Product kernel interpolation for scalable Gaussian processes

JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson

International Conference on Artificial Intelligence and Statistics, 2018

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

Deep Ensembles Work, But Are They Necessary?

T Abe, EK Buchanan, G Pleiss, R Zemel, JP Cunningham

Advances in Neural Information Processing Systems, 2022

授權規定： US National Science Foundation, US National Institutes of Health

Fast matrix square roots with applications to Gaussian processes and Bayesian optimization

G Pleiss, M Jankowiak, D Eriksson, A Damle, JR Gardner

Advances in Neural Information Processing Systems, 2020

授權規定： US National Science Foundation, Bill & Melinda Gates Foundation, US …

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization

J Wenger, G Pleiss, P Hennig, JP Cunningham, JR Gardner

International Conference on Machine Learning, 2022

授權規定： German Research Foundation, European Commission, Federal Ministry of …

[PDF] pnas.org

Harnessing Interpretable and Unsupervised Machine Learning to Address Big Data from Modern X-ray Diffraction

EA Kim, J Venderley, M Matty, K Mallayya, M Krogstad, J Ruff, G Pleiss, ...

Proceedings of the National Academy of Sciences 119 (24), e2109665119, 2022

授權規定： US National Science Foundation, US Department of Energy

Posterior and Computational Uncertainty in Gaussian Processes

J Wenger, G Pleiss, M Pförtner, P Hennig, JP Cunningham

Advances in Neural Information Processing Systems, 2022

授權規定： German Research Foundation, European Commission, Federal Ministry of …