Gavin Taylor

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 32

Top Topics

Linear Programming

Autonomous Mental Development

Reinforcement Learning

Top Venues

Publications

Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Uncertainty-aware autonomous sensing with deep reinforcement learning. Future Gener. Comput. Syst. 156 (2024)
Jonathon Parry, Donald Costello, Jason Rupert, Gavin Taylor
The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022. Syst. Eng. 26 (6) (2023)
Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. CoRR (2022)
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR (2022)
Jonas Geiping, Liam H. Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. ICLR (2021)
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting. Sensors 21 (23) (2021)
Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. ICLR (2021)
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting. CoRR (2021)
Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition. CoRR (2021)
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Information-driven adaptive sensing based on deep reinforcement learning. IOT (2020)
W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein
MetaPoison: Practical General-purpose Clean-label Data Poisoning. CoRR (2020)
Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching. CoRR (2020)
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning. CoRR (2020)
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
FLAG: Adversarial Data Augmentation for Graph Neural Networks. CoRR (2020)
W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein
MetaPoison: Practical General-purpose Clean-label Data Poisoning. NeurIPS (2020)
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. SASO (2019)
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
Adversarial Training for Free! CoRR (2019)
Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. ICML (2019)
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John P. Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
Adversarial training for free! NeurIPS (2019)
Abdulmajid Murad, Kerstin Bach, Frank Alexander Kraemer, Gavin Taylor
IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning. IOT (2019)
Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. CoRR (2019)
Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. CoRR (2019)
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
Visualizing the Loss Landscape of Neural Nets. NeurIPS (2018)
Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
Adaptive Consensus ADMM for Distributed Optimization. ICML (2017)
Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein
Visualizing the Loss Landscape of Neural Nets. CoRR (2017)
Gordon Rugg, Gavin Taylor
Hoaxing statistical features of the Voynich Manuscript. Cryptologia 41 (3) (2017)
Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein
Adaptive Consensus ADMM for Distributed Optimization. CoRR (2017)
Gavin Taylor, Zheng Xu, Tom Goldstein
Scalable Classifiers with ADMM and Transpose Reduction. AAAI Workshops (2017)
Ranjeev Mittu, Gavin Taylor, Donald A. Sofge, William F. Lawless
Introduction to the Symposium on AI and the Mitigation of Human Error. AAAI Spring Symposia (2016)
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
Training Neural Networks Without Gradients: A Scalable ADMM Approach. CoRR (2016)
Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein
Training Neural Networks Without Gradients: A Scalable ADMM Approach. ICML (2016)
Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. AISTATS (2016)
Soham De, Gavin Taylor, Tom Goldstein
Variance Reduction for Distributed Stochastic Gradient Descent. CoRR (2015)
Nitin Agarwal, Sean Andrist, Dan Bohus, Fei Fang, Laurie Fenstermacher, Lalana Kagal, Takashi Kido, Christopher Kiekintveld, William F. Lawless, Huan Liu, Andrew McCallum, Hemant Purohit, Oshani Seneviratne, Keiki Takadama, Gavin Taylor
Reports on the 2015 AAAI Spring Symposium Series. AI Mag. 36 (3) (2015)
Soham De, Gavin Taylor, Tom Goldstein
Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction. CoRR (2015)
Bharat Singh, Soham De, Yangmuzi Zhang, Thomas Goldstein, Gavin Taylor
Layer-Specific Adaptive Learning Rates for Deep Networks. ICMLA (2015)
Bharat Singh, Soham De, Yangmuzi Zhang, Thomas Goldstein, Gavin Taylor
Layer-Specific Adaptive Learning Rates for Deep Networks. CoRR (2015)
Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. CoRR (2015)
Gavin Taylor, Connor Geer, David Piekut
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy. CoRR (2014)
Weiqing Gu, Ranjeev Mittu, Julie L. Marble, Gavin Taylor, Ciara Sibley, Joseph T. Coyne, William F. Lawless
Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations. AAAI Spring Symposia (2014)
Gavin Taylor, Connor Geer, David Piekut
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy. ICML (2014)
Gavin Taylor, Ronald Parr
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. UAI (2012)
Gavin Taylor, Ronald Parr
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs CoRR (2012)
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. ICML (2010)
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes CoRR (2010)
Julian Mason, Gavin Taylor
An Intensive Introductory Robotics Course Without Prerequisites. Enabling Intelligence through Middleware (2010)
Gavin Taylor, Ronald Parr
Kernelized value function approximation for reinforcement learning. ICML (2009)
Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML (2008)