Gavin Taylor

According to our database, Gavin Taylor authored at least 35 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Uncertainty-aware autonomous sensing with deep reinforcement learning.

Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor

Future Gener. Comput. Syst., 2024

Dynamic Contrastive Learning for Time Series Representation.

Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor

CoRR, 2024

2023

The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022.

Jonathon Parry, Donald H. Costello, Jason Rupert, Gavin Taylor

Syst. Eng., November, 2023

2022

Execute Order 66: Targeted Data Poisoning for Reinforcement Learning.

Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor

CoRR, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting.

Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor

Sensors, 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

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

FLAG: Adversarial Data Augmentation for Graph Neural Networks.

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

CoRR, 2020

MetaPoison: Practical General-purpose Clean-label Data Poisoning.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Information-driven adaptive sensing based on deep reinforcement learning.

Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor

Proceedings of the IoT '20: 10th International Conference on the Internet of Things, 2020

2019

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.

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

CoRR, 2019

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

Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor

Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2019

Adversarial training for free!

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning.

Abdulmajid Murad, Kerstin Bach, Frank Alexander Kraemer, Gavin Taylor

Proceedings of the 9th International Conference on the Internet of Things, 2019

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.

Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Visualizing the Loss Landscape of Neural Nets.

Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Hoaxing statistical features of the Voynich Manuscript.

Gordon Rugg, Gavin Taylor

Cryptologia, 2017

Visualizing the Loss Landscape of Neural Nets.

Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein

CoRR, 2017

Adaptive Consensus ADMM for Distributed Optimization.

Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein

Proceedings of the 34th International Conference on Machine Learning, 2017

Scalable Classifiers with ADMM and Transpose Reduction.

Gavin Taylor, Zheng Xu, Tom Goldstein

Proceedings of the Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Training Neural Networks Without Gradients: A Scalable ADMM Approach.

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein

Proceedings of the 33nd International Conference on Machine Learning, 2016

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction.

Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Introduction to the Symposium on AI and the Mitigation of Human Error.

Ranjeev Mittu, Gavin Taylor, Donald A. Sofge, William F. Lawless

Proceedings of the 2016 AAAI Spring Symposia, 2016

2015

Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction.

Soham De, Gavin Taylor, Tom Goldstein

CoRR, 2015

Variance Reduction for Distributed Stochastic Gradient Descent.

Soham De, Gavin Taylor, Tom Goldstein

CoRR, 2015

Reports on the 2015 AAAI Spring Symposium Series.

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

AI Mag., 2015

Layer-Specific Adaptive Learning Rates for Deep Networks.

Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor

Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015

2014

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

Gavin Taylor, Connor Geer, David Piekut

Proceedings of the 31th International Conference on Machine Learning, 2014

Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations.

Weiqing Gu, Ranjeev Mittu, Julie L. Marble, Gavin Taylor, Ciara Sibley, Joseph T. Coyne, William F. Lawless

Proceedings of the 2014 AAAI Spring Symposia, 2014

2012

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

Gavin Taylor, Ronald Parr

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

2010

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

Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

An Intensive Introductory Robotics Course Without Prerequisites.

Julian Mason, Gavin Taylor

Proceedings of the Enabling Intelligence through Middleware, 2010

2009

Kernelized value function approximation for reinforcement learning.

Gavin Taylor, Ronald Parr

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

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

Proceedings of the Machine Learning, 2008