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The « Optimization and Learning » workshop will take place at Institut de Mathématiques de Toulouse (IMT) and is part of the thematic semester « Optimization » organized by Labex CIMI.
The workshop will focus on important challenges in optimization for machine learning, pertaining to the following subtopics:
Representation learning. Artificial intelligence, signal & image processing and automatic language processing, among other disciplines, motivate the development of novel methodology for matrix factorization, dictionary learning and deep learning: the workshop will present recent results on the characterization and obtention of the solutions to these non-convex problems.
Stochastic optimization. In some problems pertaining to computational statistics, signal & image processing, risk computations or learning in large dimension, the objective function is intractable or available up to some approximations. The workshop will present methodological & theoretical advances to address such settings, in the context of non-smooth and non-convex optimization and including online and distributed algorithms.
Optimization with uncertainty. The workshop will address the optimization of backbox functions, possibly in the presence of noise, by means of Gaussian Processes (GP) or bandit models. It will include a mini-course on the use of GPs for optimization, and a series of presentations on recent advances in multi-armed bandit models and GP-based procedures, with a focus on theoretical guarantees and in particular bounds on the cumulated regret.
Du : 2018-09-10
Au : 2018-09-13
Lieu : Toulouse
Site Web : http://www.cimi.univ-toulouse.fr/optimisation/en/workshop-optimization-and-machine-learning/