Wrapped Loss Function for Regularizing Nonconforming Residual Distributions
This addresses a specific issue in multi-output learning, but appears incremental as it wraps existing loss functions without introducing a fundamentally new paradigm.
The paper tackles the problem of nonconforming residual distributions in multi-output machine learning by proposing a Wrapped Loss Function, which results in faster convergence, better accuracy, and improved handling of imbalanced data.
Multi-output is essential in machine learning that it might suffer from nonconforming residual distributions, i.e., the multi-output residual distributions are not conforming to the expected distribution. In this paper, we propose "Wrapped Loss Function" to wrap the original loss function to alleviate the problem. This wrapped loss function acts just like the original loss function that its gradient can be used for backpropagation optimization. Empirical evaluations show wrapped loss function has advanced properties of faster convergence, better accuracy, and improving imbalanced data.