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cs.LG2026
Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
Mathew Mithra Noel, Arindam Banerjee, Yug D. Oswal +2
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 ap…
cs.LG2026
Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors
Arian Khorasani, Nathaniel Chen, Yug D Oswal +3
How close are neural networks to the best they could possibly do? Standard benchmarks cannot answer this because they lack access to the true posterior p(y|x). We use class-conditi…