Taylor, Gavin

15 publications

CVPR 2022 Robust Optimization as Data Augmentation for Large-Scale Graphs Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

ICLR 2021 LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P Dickerson, Gavin Taylor, Tom Goldstein

ICLR 2021 Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching Jonas Geiping, Liam H Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein

NeurIPS 2020 MetaPoison: Practical General-Purpose Clean-Label Data Poisoning W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

NeurIPS 2019 Adversarial Training for Free! Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein

ICML 2019 Transferable Clean-Label Poisoning Attacks on Deep Neural Nets Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein

NeurIPS 2018 Visualizing the Loss Landscape of Neural Nets Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

ICML 2017 Adaptive Consensus ADMM for Distributed Optimization Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein

ICML 2016 Training Neural Networks Without Gradients: A Scalable ADMM Approach Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

AISTATS 2016 Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

ICML 2014 An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy Gavin Taylor, Connor Geer, David Piekut

UAI 2012 Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs Gavin Taylor, Ronald Parr

ICML 2010 Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein

ICML 2009 Kernelized Value Function Approximation for Reinforcement Learning Gavin Taylor, Ronald Parr

ICML 2008 An Analysis of Linear Models, Linear Value-Function Approximation, and Feature Selection for Reinforcement Learning Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman