| Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration JR Gardner, G Pleiss, D Bindel, KQ Weinberger, AG Wilson Advances in Neural Information Processing Systems, 2018 | 482 | 2018 |
| Bayesian Optimization with Inequality Constraints. JR Gardner, MJ Kusner, ZE Xu, KQ Weinberger, JP Cunningham ICML 2014, 937-945, 2014 | 335 | 2014 |
| Simple black-box adversarial attacks C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger International Conference on Machine Learning, 2019 | 243 | 2019 |
| Deep feature interpolation for image content changes P Upchurch*, J Gardner*, G Pleiss, R Pless, N Snavely, K Bala, ... Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 231 | 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 | 151 | 2019 |
| Scalable global optimization via local bayesian optimization D Eriksson, M Pearce, JR Gardner, R Turner, M Poloczek Advances in Neural Information Processing Systems, 2019 | 150 | 2019 |
| Deep manifold traversal: Changing labels with convolutional features JR Gardner, P Upchurch, MJ Kusner, Y Li, KQ Weinberger, K Bala, ... arXiv preprint arXiv:1511.06421, 2015 | 73 | 2015 |
| Discovering and exploiting additive structure for Bayesian optimization J Gardner, C Guo, K Weinberger, R Garnett, R Grosse Artificial Intelligence and Statistics, 1311-1319, 2017 | 67 | 2017 |
| Constant-time predictive distributions for Gaussian processes G Pleiss, JR Gardner, KQ Weinberger, AG Wilson International Conference on Machine Learning, 2018 | 64 | 2018 |
| Differentially private Bayesian optimization M Kusner, J Gardner, R Garnett, K Weinberger International conference on machine learning, 918-927, 2015 | 53 | 2015 |
| Product kernel interpolation for scalable Gaussian processes JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson Artificial Intelligence and Statistics, 2018 | 50 | 2018 |
| A reduction of the elastic net to support vector machines with an application to GPU computing Q Zhou, W Chen, S Song, J Gardner, K Weinberger, Y Chen Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 48 | 2015 |
| Fast, continuous audiogram estimation using machine learning XD Song, BM Wallace, JR Gardner, NM Ledbetter, KQ Weinberger, ... Ear and hearing 36 (6), e326, 2015 | 42 | 2015 |
| Bayesian active model selection with an application to automated audiometry J Gardner, G Malkomes, R Garnett, KQ Weinberger, D Barbour, ... Advances in neural information processing systems 28, 2015 | 34 | 2015 |
| Parametric Gaussian Process Regressors M Jankowiak, G Pleiss, JR Gardner International Conference on Machine Learning, 2020 | 32* | 2020 |
| Psychophysical detection testing with Bayesian active learning. JR Gardner, X Song, KQ Weinberger, DL Barbour, JP Cunningham UAI, 286-295, 2015 | 27 | 2015 |
| Parallel support vector machines in practice S Tyree, JR Gardner, KQ Weinberger, K Agrawal, J Tran arXiv preprint arXiv:1404.1066, 2014 | 27 | 2014 |
| 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 | 18 | 2020 |
| Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees S Jiang, DR Jiang, M Balandat, B Karrer, JR Gardner, R Garnett Advances in Neural Information Processing Systems, 2020 | 12 | 2020 |
| Deep Sigma Point Processes M Jankowiak, G Pleiss, JR Gardner Uncertainty in Artificial Intelligence, 2020 | 8 | 2020 |
Geoff PleissColumbia UniversityVerified email at columbia.edu
