Gavin Taylor

Orcid: 0000-0002-3455-9430

According to our database1, Gavin Taylor authored at least 34 papers between 2008 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2024
Uncertainty-aware autonomous sensing with deep reinforcement learning.
Future Gener. Comput. Syst., 2024

2023
The National Airworthiness Council artificial intelligence working group (NACAIWG) summit proceedings 2022.
Syst. Eng., November, 2023

2022
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning.
CoRR, 2022

Robust Optimization as Data Augmentation for Large-scale Graphs.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting.
Sensors, 2021

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.
Proceedings of the 9th International Conference on Learning Representations, 2021

LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
FLAG: Adversarial Data Augmentation for Graph Neural Networks.
CoRR, 2020

MetaPoison: Practical General-purpose Clean-label Data Poisoning.
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.
Proceedings of the IoT '20: 10th International Conference on the Internet of Things, 2020

2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.
CoRR, 2019

Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning.
Proceedings of the 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2019

Adversarial training for free!
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.
Proceedings of the 9th International Conference on the Internet of Things, 2019

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Visualizing the Loss Landscape of Neural Nets.
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.
Cryptologia, 2017

Visualizing the Loss Landscape of Neural Nets.
CoRR, 2017

Adaptive Consensus ADMM for Distributed Optimization.
Proceedings of the 34th International Conference on Machine Learning, 2017

Scalable Classifiers with ADMM and Transpose Reduction.
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.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Introduction to the Symposium on AI and the Mitigation of Human Error.
Proceedings of the 2016 AAAI Spring Symposia, 2016

2015
Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction.
CoRR, 2015

Variance Reduction for Distributed Stochastic Gradient Descent.
CoRR, 2015

Reports on the 2015 AAAI Spring Symposium Series.
AI Mag., 2015

Layer-Specific Adaptive Learning Rates for Deep Networks.
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.
Proceedings of the 31th International Conference on Machine Learning, 2014

Towards Modeling the Behavior of Autonomous Systems and Humans for Trusted Operations.
Proceedings of the 2014 AAAI Spring Symposia, 2014

2012
Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs.
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.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

An Intensive Introductory Robotics Course Without Prerequisites.
Proceedings of the Enabling Intelligence through Middleware, 2010

2009
Kernelized value function approximation for reinforcement learning.
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.
Proceedings of the Machine Learning, 2008


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