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Geoff Pleiss
Geoff Pleiss
Verified email at columbia.edu - Homepage
Title
Cited by
Cited by
Year
On calibration of modern neural networks
C Guo, G Pleiss, Y Sun, KQ Weinberger
International Conference on Machine Learning, 1321-1330, 2017
28722017
Snapshot ensembles: Train 1, get M for free
G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger
International Conference on Learning Representations, 2017
6972017
On fairness and calibration
G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger
Advances in Neural Information Processing Systems, 2017
5812017
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
5432018
Deep feature interpolation for image content changes
P Upchurch, JR Gardner, G Pleiss, K Bala, R Pless, N Snavely, ...
Computer Vision and Pattern Recognition, 2017
2482017
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
2312019
Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
Y You, Y Wang, WL Chao, D Garg, G Pleiss, B Hariharan, M Campbell, ...
International Conference on Learning Representations, 2020
2202020
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
1602019
Memory-efficient implementation of densenets
G Pleiss, D Chen, G Huang, T Li, L Van Der Maaten, KQ Weinberger
arXiv preprint arXiv:1707.06990, 2017
1492017
Identifying mislabeled data using the area under the margin ranking
G Pleiss, T Zhang, ER Elenberg, KQ Weinberger
Advances in Neural Information Processing Systems, 2020
802020
Constant-time predictive distributions for Gaussian processes
G Pleiss, JR Gardner, KQ Weinberger, AG Wilson
International Conference on Machine Learning, 2018
672018
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
532018
Parametric Gaussian Process Regressors
M Jankowiak, G Pleiss, JR Gardner
International Conference on Machine Learning, 2020
39*2020
Uses and abuses of the cross-entropy loss: Case studies in modern deep learning
E Gordon-Rodriguez, G Loaiza-Ganem, G Pleiss, JP Cunningham
NeurIPS “I Can’t Believe It’s Not Better!” Workshop, 1-10, 2020
262020
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
242020
Potential predictability of regional precipitation and discharge extremes using synoptic-scale climate information via machine learning: An evaluation for the eastern …
J Knighton, G Pleiss, E Carter, S Lyon, MT Walter, S Steinschneider
Journal of Hydrometeorology 20 (5), 883-900, 2019
132019
Rectangular flows for manifold learning
AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham
Advances in Neural Information Processing Systems, 2021
122021
Bias-Free Scalable Gaussian Processes via Randomized Truncations
A Potapczynski, L Wu, D Biderman, G Pleiss, JP Cunningham
International Conference on Machine Learning, 2021
112021
Deep Sigma Point Processes
M Jankowiak, G Pleiss, JR Gardner
Conference on Uncertainty in Artificial Intelligence, 2020
92020
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
82022
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Articles 1–20