4/10/2023 0 Comments What is empirical risk![]() We develop batch and stochastic first-order optimization methods for solving TERM, and show that the problem can be efficiently solved relative to common alternatives. We provide several interpretations of the resulting framework: We show that TERM can increase or decrease the influence of outliers, respectively, to enable fairness or robustness has variance-reduction properties that can benefit generalization and can be viewed as a smooth approximation to a superquantile method. In particular, we show that it is possible to flexibly tune the impact of individual losses through a straightforward extension to ERM using a hyperparameter called the tilt. ![]() While many methods aim to address these problems individually, in this work, we explore them through a unified framework-tilted empirical risk minimization (TERM). Abstract: Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or treat subgroups unfairly.
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