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New loss functions enhance ML model robustness to outliers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new loss functions and regression models designed to improve robustness against outliers in machine learning datasets. The proposed approach modulates the learning rate based on outlier sensitivity, with a focus on alternate loss functions that more closely approximate absolute error than existing methods like Huber and log-cosh losses. The paper introduces Square Root Loss (SRL) and Smooth Mean Absolute Error (SMAE) as superior alternatives, demonstrating their effectiveness through comparisons on various benchmarks and presenting new robust linear regression models with vectorized parameter update formulas optimized for GPUs. AI

IMPACT Improves the reliability of machine learning models in real-world scenarios with noisy data.

RANK_REASON Academic paper detailing new methods and models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New loss functions enhance ML model robustness to outliers

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Academic paper detailing new methods and models. [lever_c_demoted from research: ic=1 ai=1.0]
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85 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal, Geraldine Bessie Amali D, Venkataraman Muthiah-Nakarajan ·

    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 …

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