Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
arXiv:2606.22068v2 Announce Type: replace-cross Abstract: Most real-world datasets used for training supervised learning models are contaminated with noisy data and outliers leading to large prediction errors. This paper proposes a new approach for achieving robustness where the learning rate is modulated by a factor that is sensitive to outliers. In this approach a reduction of the learning rate is shown to be achieved by using alternate loss functions that are infinitely differentiable, strictly convex or quasiconvex and more closely approximate the absolute error than Huber and log-cosh los
The continuous drive for more robust and reliable AI models, especially with increasing data complexity and noise in real-world applications, makes advancements in outlier-resistant learning salient.
Improved robustness to outliers in machine learning models can lead to more reliable AI systems, reducing errors and increasing trust in AI applications across various industries.
The development of novel loss functions and learning rate modulation techniques provides new tools for AI practitioners to build models that are less susceptible to noisy data and anomalous inputs.
- · AI/ML researchers
- · Industries with noisy datasets (e.g., finance, healthcare)
- · AI model developers
- · Companies relying on basic regression models
- · AI systems prone to data corruption
AI models will become more reliable and perform better in real-world, imperfect data environments.
Increased adoption of AI in critical applications where data quality is a significant concern due to reduced error rates.
A potential shift in AI development methodologies towards greater focus on data robustness rather than just model complexity.
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Read at arXiv cs.AI