Summary of Learning with Fitzpatrick Losses, by Seta Rakotomandimby et al.
Learning with Fitzpatrick Losses
by Seta Rakotomandimby, Jean-Philippe Chancelier, Michel de Lara, Mathieu Blondel
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper introduces Fitzpatrick losses, a new family of convex loss functions based on the Fitzpatrick function from maximal monotone operator theory. The Fitzpatrick losses naturally lead to a refined Fenchel-Young inequality, making them tighter than traditional Fenchel-Young losses while maintaining the same link function for prediction. As an example, the paper introduces Fitzpatrick logistic and sparsemax losses, counterparts of the logistic and sparsemax losses. These new losses are associated with the soft argmax and sparse argmax output layers, respectively. The authors study the properties of Fitzpatrick losses and demonstrate their effectiveness for label proportion estimation. This work can be seen as a refinement of Fenchel-Young losses using a modified generating function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about finding new ways to calculate how well a machine learning model does on a task. The researchers introduce a new type of loss function, called Fitzpatrick losses, which can help models do better on certain tasks. They show that these new loss functions are tighter than traditional ones, which means they can be more accurate. As an example, the paper introduces two new loss functions, one for logistic regression and one for sparsemax output layers. The authors test their new loss functions and find that they work well for a specific task called label proportion estimation. |
Keywords
» Artificial intelligence » Logistic regression » Loss function » Machine learning
