| Visualizing the loss landscape of neural nets H Li, Z Xu, G Taylor, C Studer, T Goldstein arXiv preprint arXiv:1712.09913, 2017 | 548 | 2017 |
| Adversarial training for free! A Shafahi, M Najibi, A Ghiasi, Z Xu, J Dickerson, C Studer, LS Davis, ... arXiv preprint arXiv:1904.12843, 2019 | 255 | 2019 |
| 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 | 198 | 2008 |
| 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 | 184 | 2016 |
| Kernelized value function approximation for reinforcement learning G Taylor, R Parr Proceedings of the 26th annual international conference on machine learning …, 2009 | 120 | 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 | 89 | 2010 |
| 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 | 56 | 2019 |
| 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 | 36 | 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 | 28 | 2015 |
| Unwrapping ADMM: efficient distributed computing via transpose reduction T Goldstein, G Taylor, K Barabin, K Sayre Artificial Intelligence and Statistics, 1151-1158, 2016 | 21 | 2016 |
| Super-resolution community detection for layer-aggregated multilayer networks D Taylor, RS Caceres, PJ Mucha Physical Review X 7 (3), 031056, 2017 | 20 | 2017 |
| Metapoison: Practical general-purpose clean-label data poisoning WR Huang, J Geiping, L Fowl, G Taylor, T Goldstein arXiv preprint arXiv:2004.00225, 2020 | 17 | 2020 |
| 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 | 15 | 2019 |
| 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 | 15 | 2012 |
| 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 | 8 | 2020 |
| 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 | 5 | 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 | 5 | 2016 |
| Variance reduction for distributed stochastic gradient descent S De, G Taylor, T Goldstein arXiv preprint arXiv:1512.01708, 2015 | 4 | 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 | 4 | 2014 |
| 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 | 3 | 2021 |
