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Execute Order 66: Targeted Data Poisoning for Reinforcement Learning

Jan 03, 2022
Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor

* Workshop on Safe and Robust Control of Uncertain Systems at the 35th Conference on Neural Information Processing Systems 

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Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Dec 05, 2021
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor

* Sensors, 21(23) 2021) 

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LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition

Jan 25, 2021
Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John Dickerson, Gavin Taylor, Tom Goldstein

* Published as a conference paper at ICLR 2021 

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FLAG: Adversarial Data Augmentation for Graph Neural Networks

Oct 19, 2020
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

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Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning

Oct 08, 2020
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor

* 10th International Conference on the Internet of Things (IoT20), October 6-9, 2020, Malmo, Sweden 
* 8 pages, 8 figures 

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Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Sep 04, 2020
Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein

* First two authors contributed equally. Last two authors contributed equally. 21 pages, 11 figures 

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MetaPoison: Practical General-purpose Clean-label Data Poisoning

Apr 01, 2020
W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

* First two authors contributed equally 

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Transferable Clean-Label Poisoning Attacks on Deep Neural Nets

May 16, 2019
Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein

* Accepted to ICML2019 

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Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning

May 10, 2019
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor

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Adversarial Training for Free!

Apr 29, 2019
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein

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Visualizing the Loss Landscape of Neural Nets

Mar 05, 2018
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

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Adaptive Consensus ADMM for Distributed Optimization

Jun 20, 2017
Zheng Xu, Gavin Taylor, Hao Li, Mario Figueiredo, Xiaoming Yuan, Tom Goldstein

* ICML 2017 

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Variance Reduction for Distributed Stochastic Gradient Descent

Apr 07, 2017
Soham De, Gavin Taylor, Tom Goldstein

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Training Neural Networks Without Gradients: A Scalable ADMM Approach

May 06, 2016
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

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Layer-Specific Adaptive Learning Rates for Deep Networks

Oct 15, 2015
Bharat Singh, Soham De, Yangmuzi Zhang, Thomas Goldstein, Gavin Taylor

* ICMLA 2015, deep learning, adaptive learning rates for training, layer specific learning rate 

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Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction

Apr 08, 2015
Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

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An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy

Apr 24, 2014
Gavin Taylor, Connor Geer, David Piekut

* Identical to the ICML 2014 paper of the same name, but with full proofs. Please cite the ICML paper 

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Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs

Oct 16, 2012
Gavin Taylor, Ron Parr

* Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012) 

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Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes

May 20, 2010
Marek Petrik, Gavin Taylor, Ron Parr, Shlomo Zilberstein

* Technical report corresponding to the ICML2010 submission of the same name 

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