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July 11 2010 1 11 /07 /July /2010 20:40

This page is dedicated to start discussions about the article "Online Learning in Adversarial Lipschitz Environments".
Feel free to post any comment, sugggestion, question, correction, extension... I will enjoy discussing this with you.

  • Abstract:

"We consider the problem of online learning in an adversarial environment when the reward functions chosen by the adversary are assumed to be Lipschitz. This setting extends previous works on linear and convex online learning. We  provide a class of algorithms with cumulative regret upper bounded by O(\sqrt{dT ln(\lambda)}) where d is the dimension of the search space, T the time horizon, and \lambda the Lipschitz constant. Efficient numerical implementations using particle methods are discussed. Applications include online supervised learning problems for both full and partial (bandit) information settings, for a large class of non-linear regressors/classifiers, such as neural networks."

  • Discussion:

It would be interesting to analyse general PMC methods and get full understanding of the speed of convergence towards the distribution targetted by the method. This involves working for instance on further developing the nice results of the book (Feynman-Kac Formulae, Pierre Del Moral,2004) as well as (Sequential Monte Carlo Methods in Practice, A.Doucet , N. de Freitas, N. Gordon, 2001).

  • Future work: 

Developping a deeper analysis of the PMC methods, that can be applied to this paper, or any other else.

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Published by Odalric-Ambrym Maillard - in Discussing articles
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