# Gavin Taylor

According to our database

Collaborative distances:

^{1}, Gavin Taylor authored at least 25 papers between 2008 and 2020.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Other## Links

#### On csauthors.net:

## Bibliography

2020

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

CoRR, 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 Magazine, 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