Jacob Gardner
Title
Cited by
Cited by
Year
Bayesian Optimization with Inequality Constraints.
JR Gardner, MJ Kusner, ZE Xu, KQ Weinberger, JP Cunningham
ICML 2014, 937-945, 2014
2402014
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
2002018
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
1702017
Simple black-box adversarial attacks
C Guo, JR Gardner, Y You, AG Wilson, KQ Weinberger
International Conference on Machine Learning, 2019
1142019
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
722019
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
612015
Scalable global optimization via local bayesian optimization
D Eriksson, M Pearce, JR Gardner, R Turner, M Poloczek
Advances in Neural Information Processing Systems, 2019
462019
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
452017
Differentially private Bayesian optimization
M Kusner, J Gardner, R Garnett, K Weinberger
International conference on machine learning, 918-927, 2015
402015
Fast, continuous audiogram estimation using machine learning
XD Song, BM Wallace, JR Gardner, NM Ledbetter, KQ Weinberger, ...
Ear and hearing 36 (6), e326, 2015
392015
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
392015
Product kernel interpolation for scalable Gaussian processes
JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson
Artificial Intelligence and Statistics, 2018
372018
Constant-time predictive distributions for Gaussian processes
G Pleiss, JR Gardner, KQ Weinberger, AG Wilson
International Conference on Machine Learning, 2018
362018
Bayesian active model selection with an application to automated audiometry
JR Gardner, G Malkomes, R Garnett, KQ Weinberger, D Barbour, ...
Proceedings of the 28th International Conference on Neural Information …, 2015
292015
Parallel support vector machines in practice
S Tyree, JR Gardner, KQ Weinberger, K Agrawal, J Tran
arXiv preprint arXiv:1404.1066, 2014
242014
Psychophysical Detection Testing with Bayesian Active Learning.
JR Gardner, X Song, KQ Weinberger, DL Barbour, JP Cunningham
UAI, 286-295, 2015
222015
Parametric Gaussian Process Regressors
M Jankowiak, G Pleiss, JR Gardner
International Conference on Machine Learning, 2020
11*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
42020
Deep Sigma Point Processes
M Jankowiak, G Pleiss, JR Gardner
Uncertainty in Artificial Intelligence, 2020
42020
Compressed support vector machines
Z Xu, JR Gardner, S Tyree, KQ Weinberger
arXiv preprint arXiv:1501.06478, 2015
42015
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Articles 1–20