Geoff Pleiss

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Kilian WeinbergerCornell UniversityVerified email at cornell.edu
Jacob GardnerUniversity of PennsylvaniaVerified email at seas.upenn.edu
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
John CunninghamProfessor, Columbia UniversityVerified email at columbia.edu
Gao Huang （黄高）Associate Professor, Tsinghua UniversityVerified email at tsinghua.edu.cn
Yu SunPostdoc, Stanford UniversityVerified email at cs.stanford.edu
Chuan GuoResearch Scientist, FAIR, MetaVerified email at fb.com
John HopcroftCornell UniversityVerified email at cs.cornell.edu
Laurens van der MaatenDistinguished Research Scientist, Llama Team, Meta AIVerified email at meta.com
Martin JankowiakGenerate BiomedicinesVerified email at generatebiomedicines.com
David BindelProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Sharon LiUniversity of Wisconsin-MadisonVerified email at cs.wisc.edu
Felix WuCharacter AIVerified email at character.ai
Jon KleinbergProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Manish RaghavanMITVerified email at mit.edu
Zhuang LiuResearch Scientist, FAIR, MetaVerified email at berkeley.edu
Wei-Lun (Harry) Chao 趙偉崙Assistant Professor, The Ohio State UniversityVerified email at osu.edu
Yan WangSenior Research Scientist, NVIDIA ResearchVerified email at nvidia.com
bharath hariharanCornell UniversityVerified email at cs.cornell.edu
Gabriel Loaiza-GanemLayer 6 AIVerified email at layer6.ai

Geoff Pleiss

Assistant Professor, University of British Columbia

Verified email at stat.ubc.ca - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by 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	6505	2017
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	1366	2018
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	1151	2017
On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in Neural Information Processing Systems, 2017	1086	2017
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	551	2019
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	470	2020
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	370	2017
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	284	2019
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	272	2020
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	205	2017
Constant-time predictive distributions for Gaussian processes G Pleiss, JR Gardner, KQ Weinberger, AG Wilson International Conference on Machine Learning, 2018	131	2018
Parametric Gaussian Process Regressors M Jankowiak, G Pleiss, JR Gardner International Conference on Machine Learning, 2020	89	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	89	2020
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	89	2018
Deep Ensembles Work, But Are They Necessary? T Abe, EK Buchanan, G Pleiss, R Zemel, JP Cunningham Advances in Neural Information Processing Systems, 2022	59	2022
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	55	2020
Snapshot ensembles: Train 1, get m for free,” 2017 G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger arXiv preprint arXiv:1704.00109, 1-14, 2017	54	2017
Rectangular flows for manifold learning AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham Advances in Neural Information Processing Systems, 2021	42	2021
Variational Nearest Neighbor Gaussian Processes L Wu, G Pleiss, JP Cunningham International Conference on Machine Learning, 2022	38	2022
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization J Wenger, G Pleiss, P Hennig, JP Cunningham, JR Gardner International Conference on Machine Learning, 2022	32*	2022

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