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, 6389-6399, 2018
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
Adversarial training for free!
A Shafahi, M Najibi, MA Ghiasi, Z Xu, J Dickerson, C Studer, LS Davis, ...
Advances in Neural Information Processing Systems, 3358-3369, 2019
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
Kernelized value function approximation for reinforcement learning
G Taylor, R Parr
Proceedings of the 26th annual international conference on machine learning …, 2009
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
Transferable clean-label poisoning attacks on deep neural nets
C Zhu, WR Huang, A Shafahi, H Li, G Taylor, C Studer, T Goldstein
arXiv preprint arXiv:1905.05897, 2019
Adaptive consensus ADMM for distributed optimization
Z Xu, G Taylor, H Li, M Figueiredo, X Yuan, T Goldstein
arXiv preprint arXiv:1706.02869, 2017
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
Unwrapping ADMM: efficient distributed computing via transpose reduction
T Goldstein, G Taylor, K Barabin, K Sayre
Artificial Intelligence and Statistics, 1151-1158, 2016
Super-resolution community detection for layer-aggregated multilayer networks
D Taylor, RS Caceres, PJ Mucha
Physical Review X 7 (3), 031056, 2017
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
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
MetaPoison: Practical General-purpose Clean-label Data Poisoning
WR Huang, J Geiping, L Fowl, G Taylor, T Goldstein
arXiv preprint arXiv:2004.00225, 2020
Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations
G Taylor, R Mittu, C Sibley, J Coyne
Robust Intelligence and Trust in Autonomous Systems, 11-31, 2016
Variance reduction for distributed stochastic gradient descent
S De, G Taylor, T Goldstein
arXiv preprint arXiv:1512.01708, 2015
Towards modeling the behavior of autonomous systems and humans for trusted operations
W Gu, R Mittu, J Marble, G Taylor, C Sibley, J Coyne, WF Lawless
2014 AAAI Spring Symposium Series, 2014
Scalable classifiers with ADMM and transpose reduction
G Taylor, Z Xu, T Goldstein
AAAI Workshops, 2017
Feature selection for value function approximation
G Taylor
Duke University, 2011
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 preprint arXiv:2010.09891, 2020
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