Optimization Problems for Machine Learning: A Survey
It provides a structured overview for researchers and practitioners in machine learning, but it is incremental as it synthesizes existing literature without new results.
This survey paper organizes various machine learning approaches, including regression, classification, clustering, deep learning, and adversarial learning, into an optimization framework, discussing their strengths, shortcomings, and open problems.
This paper surveys the machine learning literature and presents in an optimization framework several commonly used machine learning approaches. Particularly, mathematical optimization models are presented for regression, classification, clustering, deep learning, and adversarial learning, as well as new emerging applications in machine teaching, empirical model learning, and Bayesian network structure learning. Such models can benefit from the advancement of numerical optimization techniques which have already played a distinctive role in several machine learning settings. The strengths and the shortcomings of these models are discussed and potential research directions and open problems are highlighted.