| 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 | 1462 | 2018 |
| Simple black-box adversarial attacks C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger International Conference on Machine Learning, 2019 | 690 | 2019 |
| Bayesian Optimization with Inequality Constraints. JR Gardner, MJ Kusner, ZE Xu, KQ Weinberger, JP Cunningham ICML 2014, 937-945, 2014 | 647 | 2014 |
| Scalable global optimization via local bayesian optimization D Eriksson, M Pearce, JR Gardner, R Turner, M Poloczek Advances in Neural Information Processing Systems, 2019 | 598 | 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 | 379 | 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 | 293 | 2019 |
| 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 | 139 | 2017 |
| Constant-time predictive distributions for Gaussian processes G Pleiss, JR Gardner, KQ Weinberger, AG Wilson International Conference on Machine Learning, 2018 | 134 | 2018 |
| Adversarial prompting for black box foundation models N Maus, P Chao, E Wong, J Gardner arXiv preprint arXiv:2302.04237 1 (2), 2023 | 109* | 2023 |
| Parametric Gaussian Process Regressors M Jankowiak, G Pleiss, JR Gardner International Conference on Machine Learning, 2020 | 102 | 2020 |
| Product kernel interpolation for scalable Gaussian processes JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson Artificial Intelligence and Statistics, 2018 | 91 | 2018 |
| 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 | 91 | 2015 |
| Local Latent Space Bayesian Optimization over Structured Inputs N Maus, HT Jones, JS Moore, MJ Kusner, J Bradshaw, JR Gardner Advances in Neural Information Processing Systems, 2022 | 79 | 2022 |
| Fast, continuous audiogram estimation using machine learning XD Song, BM Wallace, JR Gardner, NM Ledbetter, KQ Weinberger, ... Ear and hearing 36 (6), e326-e335, 2015 | 75 | 2015 |
| Differentially private Bayesian optimization M Kusner, J Gardner, R Garnett, K Weinberger International conference on machine learning, 918-927, 2015 | 68 | 2015 |
| 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 | 64 | 2020 |
| 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 | 64 | 2015 |
| 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 | 62 | 2020 |
| Learning performance-improving code edits A Shypula, A Madaan, Y Zeng, U Alon, J Gardner, M Hashemi, G Neubig, ... International Conference on Learning Representations, 2024 | 61 | 2024 |
| 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 | 56 | 2015 |
Kilian WeinbergerCornell UniversityEmail confirmado em cornell.edu