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arXiv · 2301.05579

A survey and taxonomy of loss functions in machine learning

Lorenzo Ciampiconi·Adam Elwood·Marco Leonardi·Ashraf Mohamed·Alessandro Rozza
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Abstract

Most state-of-the-art machine learning techniques revolve around the optimisation of loss functions. Defining appropriate loss functions is therefore critical to successfully solving problems in this field. In this survey, we present a comprehensive overview of the most widely used loss functions across key applications, including regression, classification, generative modeling, ranking, and energy-based modeling. We introduce 43 distinct loss functions, structured within an intuitive taxonomy that clarifies their theoretical foundations, properties, and optimal application contexts. This survey is intended as a resource for undergraduate, graduate, and Ph.D. students, as well as researchers seeking a deeper understanding of loss functions.

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Lorenzo Ciampiconi, Adam Elwood, Marco Leonardi, Ashraf Mohamed, Alessandro Rozza. 2023-01-13. A survey and taxonomy of loss functions in machine learning. https://arxiv.org/abs/2301.05579

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