Guy Lever

According to our database1, Guy Lever authored at least 22 papers between 2008 and 2018.

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Bibliography

2018
Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.
CoRR, 2018

Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward.
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

2017
Value-Decomposition Networks For Cooperative Multi-Agent Learning.
CoRR, 2017

Nesterov's accelerated gradient and momentum as approximations to regularised update descent.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

2016
Approximate Newton Methods for Policy Search in Markov Decision Processes.
Journal of Machine Learning Research, 2016

Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent.
CoRR, 2016

Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
A Gauss-Newton Method for Markov Decision Processes.
CoRR, 2015

Modelling Policies in MDPs in Reproducing Kernel Hilbert Space.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Deterministic Policy Gradient Algorithms.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Tighter PAC-Bayes bounds through distribution-dependent priors.
Theor. Comput. Sci., 2013

2012
Data dependent kernels in nearly-linear time.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Modelling transition dynamics in MDPs with RKHS embeddings
CoRR, 2012

Conditional mean embeddings as regressors - supplementary
CoRR, 2012

Conditional mean embeddings as regressors.
Proceedings of the 29th International Conference on Machine Learning, 2012

Modelling transition dynamics in MDPs with RKHS embeddings.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Exploiting structure defined by data in machine learning : some new analyses.
PhD thesis, 2011

Data-dependent kernels in nearly-linear time
CoRR, 2011

2010
Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Distribution-Dependent PAC-Bayes Priors.
Proceedings of the Algorithmic Learning Theory, 21st International Conference, 2010

2009
Predicting the Labelling of a Graph via Minimum $p$-Seminorm Interpolation.
Proceedings of the COLT 2009, 2009

2008
Online Prediction on Large Diameter Graphs.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008


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