| On calibration of modern neural networks C Guo, G Pleiss, Y Sun, KQ Weinberger International Conference on Machine Learning, 1321-1330, 2017 | 2377 | 2017 |
| 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 | 626 | 2017 |
| On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in Neural Information Processing Systems, 2017 | 497 | 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 | 441 | 2018 |
| Deep feature interpolation for image content changes P Upchurch, J Gardner, G Pleiss, K Bala, R Pless, N Snavely, ... Computer Vision and Pattern Recognition, 2016 | 226 | 2016 |
| Convolutional Networks with Dense Connectivity G Huang, Z Liu, G Pleiss, L Van Der Maaten, K Weinberger Transactions on Pattern Analysis and Machine Intelligence, 2019 | 185 | 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, 2019 | 184 | 2019 |
| 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 | 139 | 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 | 138 | 2019 |
| Constant-time predictive distributions for Gaussian processes G Pleiss, JR Gardner, KQ Weinberger, AG Wilson International Conference on Machine Learning, 2018 | 61 | 2018 |
| 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 | 60 | 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 | 50 | 2018 |
| Parametric Gaussian Process Regressors M Jankowiak, G Pleiss, JR Gardner International Conference on Machine Learning, 2019 | 29* | 2019 |
| 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, 2020 | 20 | 2020 |
| 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 | 16 | 2020 |
| 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 | 13 | 2019 |
| Deep Sigma Point Processes M Jankowiak, G Pleiss, JR Gardner Conference on Uncertainty in Artificial Intelligence, 2020 | 8 | 2020 |
| Bias-Free Scalable Gaussian Processes via Randomized Truncations A Potapczynski, L Wu, D Biderman, G Pleiss, JP Cunningham International Conference on Machine Learning, 2021 | 7 | 2021 |
| The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective G Pleiss, JP Cunningham Advances in Neural Information Processing Systems, 2021 | 5 | 2021 |
| Rectangular flows for manifold learning AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham Advances in Neural Information Processing Systems, 2021 | 5 | 2021 |
Kilian WeinbergerCornell UniversityVerified email at cornell.edu
