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The original was posted on /r/machinelearning by /u/alexsht1 on 2024-06-16 07:27:02+00:00.
Some time ago I read a paper about the so-called tilted empirical risk minimization, and later a JMLR paper from the same authors:
Such a formulation allows us to train in a manner that is more ‘fair’ towards the difficult samples, or conversely, less sensitive to these difficult samples if they are actually outliers. But minimizing it is numerically challenging. So I decided to try and devise a remedy in a blog post. I think it’s an interesting trick that is useful here, and I hope you’ll find it nice as well:
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