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Optimization for Machine Learning

Optimization lies at the heart of ML algorithms. Sometimes, classical textbook algorithms suffice, but the majority problems require tailored methods that are based on a deeper understanding of the ML requirements. ML applications and researchers are driving some of the most cutting-edge developments in optimization today. The intimate relation of optimization with ML is the key motivation for our workshop, which aims to foster discussion, discovery, and dissemination of the state-of-the-art in optimization as relevant to machine learning.

Much interest has focused recently on stochastic methods, which can be used in an online setting and in settings where data sets are extremely large and high accuracy is not required. Many aspects of stochastic gradient remain to be explored, for example, different algorithmic variants, customizing to the data set structure, convergence analysis, sampling techniques, software, choice of regularization and tradeoff parameters, distributed and parallel computation. The need for an up-to-date analysis of algorithms for nonconvex problems remains an important practical issue, whose importance becomes even more pronounced as ML tackles more and more complex mathematical models.

Finally, we do not wish to ignore the not particularly large scale setting, where one does have time to wield substantial computational resources. In this setting, high-accuracy solutions and deep understanding of the lessons contained in the data are needed. Examples valuable to MLers may be exploration of genetic and environmental data to identify risk factors for disease; or problems dealing with setups where the amount of observed data is not huge, but the mathematical model is complex.

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