| 2025 | ECAI | Contrast All The Time: Learning Time Series Representation from Temporal Consistency. | Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor |
| 2022 | CVPR | 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 |
| 2021 | ICLR | 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 |
| 2021 | ICLR | 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 |
| 2019 | ICML | Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. | Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein |
| 2017 | AAAI | Scalable Classifiers with ADMM and Transpose Reduction. | Gavin Taylor, Zheng Xu, Tom Goldstein |
| 2017 | ICML | Adaptive Consensus ADMM for Distributed Optimization. | Zheng Xu, Gavin Taylor, Hao Li, Mrio A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein |
| 2016 | AISTATS | Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. | Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre |
| 2016 | ICML | Training Neural Networks Without Gradients: A Scalable ADMM Approach. | Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein |
| 2015 | ICMLA | Layer-Specific Adaptive Learning Rates for Deep Networks. | Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor |
| 2014 | ICML | An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy. | Gavin Taylor, Connor Geer, David Piekut |
| 2012 | UAI | Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. | Gavin Taylor, Ronald Parr |
| 2010 | AAAI | An Intensive Introductory Robotics Course Without Prerequisites. | Julian Mason, Gavin Taylor |
| 2010 | ICML | Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. | Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein |
| 2009 | ICML | Kernelized value function approximation for reinforcement learning. | Gavin Taylor, Ronald Parr |
| 2008 | ICML | 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 |