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ORCIDGavin Taylor
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2020 – today
- 2024
[j5]Abdulmajid Murad
, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor:
Uncertainty-aware autonomous sensing with deep reinforcement learning. Future Gener. Comput. Syst. 156: 242-253 (2024)- [i21]Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor:
Dynamic Contrastive Learning for Time Series Representation. CoRR abs/2410.15416 (2024) - 2023
- [j4]Jonathon Parry, Donald H. Costello, Jason Rupert, Gavin Taylor:
The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022. Syst. Eng. 26(6): 925-930 (2023) - 2022
- [c23]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: 60-69 - [i20]Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor:
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning. CoRR abs/2201.00762 (2022) - 2021
- [j3]Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor:
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting. Sensors 21(23): 8009 (2021) - [c22]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 - [c21]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 - [i19]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 abs/2101.07922 (2021) - [i18]Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor:
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting. CoRR abs/2112.02622 (2021) - 2020
- [c20]Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor:
Information-driven adaptive sensing based on deep reinforcement learning. IOT 2020: 2:1-2:8 - [c19]W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein:
MetaPoison: Practical General-purpose Clean-label Data Poisoning. NeurIPS 2020 - [i17]W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein:
MetaPoison: Practical General-purpose Clean-label Data Poisoning. CoRR abs/2004.00225 (2020) - [i16]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 abs/2009.02276 (2020) - [i15]Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor:
Information-Driven Adaptive Sensing Based on Deep Reinforcement Learning. CoRR abs/2010.04112 (2020) - [i14]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 abs/2010.09891 (2020)
2010 – 2019
- 2019
- [c18]Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein:
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. ICML 2019: 7614-7623 - [c17]Abdulmajid Murad, Kerstin Bach, Frank Alexander Kraemer, Gavin Taylor:
IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning. IOT 2019: 37:1-37:4 - [c16]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: 3353-3364 - [c15]Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor:
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. SASO 2019: 43-51 - [i13]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 abs/1904.12843 (2019) - [i12]Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor:
Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning. CoRR abs/1905.04181 (2019) - [i11]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 abs/1905.05897 (2019) - 2018
- [c14]Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein:
Visualizing the Loss Landscape of Neural Nets. NeurIPS 2018: 6391-6401 - 2017
- [j2]Gordon Rugg, Gavin Taylor:
Hoaxing statistical features of the Voynich Manuscript. Cryptologia 41(3): 247-268 (2017) - [c13]Gavin Taylor, Zheng Xu, Tom Goldstein:
Scalable Classifiers with ADMM and Transpose Reduction. AAAI Workshops 2017 - [c12]Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein:
Adaptive Consensus ADMM for Distributed Optimization. ICML 2017: 3841-3850 - [i10]Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein:
Adaptive Consensus ADMM for Distributed Optimization. CoRR abs/1706.02869 (2017) - [i9]Hao Li, Zheng Xu, Gavin Taylor, Tom Goldstein:
Visualizing the Loss Landscape of Neural Nets. CoRR abs/1712.09913 (2017) - 2016
- [c11]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 - [c10]Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre:
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. AISTATS 2016: 1151-1158 - [c9]Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein:
Training Neural Networks Without Gradients: A Scalable ADMM Approach. ICML 2016: 2722-2731 - [i8]Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit B. Patel, Tom Goldstein:
Training Neural Networks Without Gradients: A Scalable ADMM Approach. CoRR abs/1605.02026 (2016) - 2015
- [j1]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): 113-119 (2015) - [c8]Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor:
Layer-Specific Adaptive Learning Rates for Deep Networks. ICMLA 2015: 364-368 - [i7]Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre:
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction. CoRR abs/1504.02147 (2015) - [i6]Bharat Singh, Soham De, Yangmuzi Zhang, Thomas A. Goldstein, Gavin Taylor:
Layer-Specific Adaptive Learning Rates for Deep Networks. CoRR abs/1510.04609 (2015) - [i5]Soham De, Gavin Taylor, Tom Goldstein:
Variance Reduction for Distributed Stochastic Gradient Descent. CoRR abs/1512.01708 (2015) - [i4]Soham De, Gavin Taylor, Tom Goldstein:
Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction. CoRR abs/1512.02970 (2015) - 2014
- [c7]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 - [c6]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: 451-459 - [i3]Gavin Taylor, Connor Geer, David Piekut:
An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy. CoRR abs/1404.4258 (2014) - 2012
- [c5]Gavin Taylor, Ronald Parr:
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. UAI 2012: 835-842 - [i2]Gavin Taylor, Ronald Parr:
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. CoRR abs/1210.4898 (2012) - 2010
- [c4]Julian Mason, Gavin Taylor:
An Intensive Introductory Robotics Course Without Prerequisites. Enabling Intelligence through Middleware 2010 - [c3]Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. ICML 2010: 871-878 - [i1]Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zilberstein:
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes. CoRR abs/1005.1860 (2010)
2000 – 2009
- 2009
- [c2]Gavin Taylor, Ronald Parr:
Kernelized value function approximation for reinforcement learning. ICML 2009: 1017-1024 - 2008
- [c1]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: 752-759
Coauthor Index
aka: Thomas A. Goldstein
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