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Gavin Taylor
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Visualizing the loss landscape of neural nets
H Li, Z Xu, G Taylor, C Studer, T Goldstein
Advances in neural information processing systems 31, 2018
19882018
Adversarial training for free!
A Shafahi, M Najibi, MA Ghiasi, Z Xu, J Dickerson, C Studer, LS Davis, ...
Advances in neural information processing systems 32, 2019
14232019
Transferable clean-label poisoning attacks on deep neural nets
C Zhu, WR Huang, H Li, G Taylor, C Studer, T Goldstein
International conference on machine learning, 7614-7623, 2019
3312019
Training neural networks without gradients: A scalable admm approach
G Taylor, R Burmeister, Z Xu, B Singh, A Patel, T Goldstein
International conference on machine learning, 2722-2731, 2016
3072016
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
R Parr, L Li, G Taylor, C Painter-Wakefield, ML Littman
Proceedings of the 25th international conference on Machine learning, 752-759, 2008
2622008
Witches' brew: Industrial scale data poisoning via gradient matching
J Geiping, L Fowl, WR Huang, W Czaja, G Taylor, M Moeller, T Goldstein
arXiv preprint arXiv:2009.02276, 2020
1982020
Metapoison: Practical general-purpose clean-label data poisoning
WR Huang, J Geiping, L Fowl, G Taylor, T Goldstein
Advances in Neural Information Processing Systems 33, 12080-12091, 2020
1892020
Lowkey: Leveraging adversarial attacks to protect social media users from facial recognition
V Cherepanova, M Goldblum, H Foley, S Duan, J Dickerson, G Taylor, ...
arXiv preprint arXiv:2101.07922, 2021
1332021
Kernelized value function approximation for reinforcement learning
G Taylor, R Parr
Proceedings of the 26th annual international conference on machine learning …, 2009
1332009
Flag: Adversarial data augmentation for graph neural networks
K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein
arXiv, 2020
1172020
Feature selection using regularization in approximate linear programs for Markov decision processes
M Petrik, G Taylor, R Parr, S Zilberstein
arXiv preprint arXiv:1005.1860, 2010
902010
Adaptive consensus ADMM for distributed optimization
Z Xu, G Taylor, H Li, MAT Figueiredo, X Yuan, T Goldstein
International Conference on Machine Learning, 3841-3850, 2017
832017
Robust optimization as data augmentation for large-scale graphs
K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022
772022
Layer-specific adaptive learning rates for deep networks
B Singh, S De, Y Zhang, T Goldstein, G Taylor
2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015
642015
Autonomous management of energy-harvesting iot nodes using deep reinforcement learning
A Murad, FA Kraemer, K Bach, G Taylor
2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing …, 2019
422019
Information-driven adaptive sensing based on deep reinforcement learning
A Murad, FA Kraemer, K Bach, G Taylor
Proceedings of the 10th International Conference on the Internet of Things, 1-8, 2020
252020
Unwrapping ADMM: efficient distributed computing via transpose reduction
T Goldstein, G Taylor, K Barabin, K Sayre
Artificial Intelligence and Statistics, 1151-1158, 2016
242016
Comparison of international normalized ratio audit parameters in patients enrolled in GARFIELD‐AF and treated with vitamin K antagonists
DA Fitzmaurice, G Accetta, S Haas, G Kayani, H Lucas Luciardi, ...
British journal of haematology 174 (4), 610-623, 2016
202016
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs
G Taylor, R Parr
The Conference on Uncertainty in Artificial Intelligence, 2012
132012
IoT sensor gym: Training autonomous IoT devices with deep reinforcement learning
A Murad, K Bach, FA Kraemer, G Taylor
Proceedings of the 9th International Conference on the Internet of Things, 1-4, 2019
122019
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Articles 1–20