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 | 1206 | 2018 |
Simple black-box adversarial attacks C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger International Conference on Machine Learning, 2019 | 599 | 2019 |
Bayesian Optimization with Inequality Constraints. JR Gardner, MJ Kusner, ZE Xu, KQ Weinberger, JP Cunningham ICML 2014, 937-945, 2014 | 558 | 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 | 467 | 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 | 362 | 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 | 268 | 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 | 119 | 2017 |
Constant-time predictive distributions for Gaussian processes G Pleiss, JR Gardner, KQ Weinberger, AG Wilson International Conference on Machine Learning, 2018 | 118 | 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 | 88 | 2015 |
Product kernel interpolation for scalable Gaussian processes JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson Artificial Intelligence and Statistics, 2018 | 84 | 2018 |
Parametric Gaussian Process Regressors M Jankowiak, G Pleiss, JR Gardner International Conference on Machine Learning, 2020 | 78 | 2020 |
Adversarial prompting for black box foundation models N Maus, P Chao, E Wong, J Gardner arXiv preprint arXiv:2302.04237 1 (2), 2023 | 75* | 2023 |
Differentially private Bayesian optimization M Kusner, J Gardner, R Garnett, K Weinberger International conference on machine learning, 918-927, 2015 | 68 | 2015 |
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 | 65 | 2015 |
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 | 60 | 2024 |
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 | 60 | 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 | 57 | 2022 |
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 | 50 | 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 | 48 | 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 | 46 | 2020 |