We propose a new GL model with the help of the tilted ERM criterion, and establish its theoretical support from the function approximation viewpoint.
Jul 9, 2022 · Gradient Learning (GL), aiming to estimate the gradient of target function, has attracted much attention in variable selection problems due to ...
Oct 22, 2024 · Gradient Learning (GL), aiming to estimate the gradient of target function, has attracted much attention in variable selection problems due ...
A new GL model is proposed with the help of the tilted ERM criterion, and its theoretical support from the function approximation viewpoint is established, ...
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Jul 9, 2022 · Abstract: Gradient Learning (GL), aiming to estimate the gradient of target function, has attracted much attention in variable selection ...
Apr 2, 2021 · Our work explores tilted empirical risk minimization (TERM), a simple and general alternative to ERM, which is ubiquitous throughout machine learning.
Jan 12, 2021 · We show that tilted empirical risk minimization (TERM) can be used for enforcing fairness between subgroups, mitigating the effect of outliers, and handling ...
Jun 16, 2024 · Some time ago I read a paper about the so-called tilted empirical risk minimization, and later a JMLR paper from the same authors: ...
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Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, ...
This work shows that it is possible to flexibly tune the impact of individual losses through a straightforward extension to ERM using a hyperparameter ...