# Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation Source: [Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:08:06.137Z Authors: Louis L. Chen, Bobbie Chern, Eric Eckstrand, Amogh Mahapatra, Johannes O. Royset Published: 2024-05-30T23:13:01.000Z ## Abstract Labeling errors in datasets are common, arising in a variety of contexts, such as human labeling, noisy labeling, and weak labeling (i.e., image classification). Although neural networks (NNs) can tolerate modest amounts of these errors, their performance degrades substantially once error levels exceed a certain threshold. We propose a new loss reweighting, architecture-independent methodology, Rockafellian Relaxation Method (RRM) for neural network training. Experiments indicate RRM can enhance neural network methods to achieve robust performance across classification tasks in computer vision and natural language processing (sentiment analysis). We find that RRM can mitigate the effects of dataset contamination stemming from both (heavy) labeling error and/or adversarial perturbation, demonstrating effectiveness across a variety of data domains and machine learning tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()