Gavin Taylor

According to our database1, Gavin Taylor authored at least 25 papers between 2008 and 2017.

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Timeline

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Bibliography

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.
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.
CoRR, 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
Layer-Specific Adaptive Learning Rates for Deep Networks.
CoRR, 2015

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction.
CoRR, 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.
CoRR, 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
CoRR, 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
CoRR, 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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