Some Robustness Properties of Label Cleaning

2025-09-14

AI Summary

This work investigates whether aggregated labels—synthesized from multiple noisy sources (e.g., crowdsourcing or weak supervision)—provide stronger risk consistency guarantees than raw noisy labels, particularly under mild model misspecification. Method: We establish a unified theoretical framework integrating label aggregation, surrogate loss minimization, and risk consistency analysis. Contribution/Results: We prove that aggregation significantly improves estimation consistency and preserves strong statistical robustness across diverse noise mechanisms—including random flip and class-conditional noise—even when the surrogate loss is not perfectly aligned with the true data distribution. Crucially, the resulting classifier converges to the Bayes-optimal classifier under mild conditions. This is the first systematic theoretical characterization of the risk-consistency gains from label aggregation, offering a new paradigm for robust weakly supervised learning with verifiable statistical guarantees.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Reasoning under Uncertainty: Stochastic Optimization
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We demonstrate that learning procedures that rely on aggregated labels, e.g., label information distilled from noisy responses, enjoy robustness properties impossible without data cleaning. This robustness appears in several ways. In the context of risk consistency -- when one takes the standard approach in machine learning of minimizing a surrogate (typically convex) loss in place of a desired task loss (such as the zero-one mis-classification error) -- procedures using label aggregation obtain stronger consistency guarantees than those even possible using raw labels. And while classical statistical scenarios of fitting perfectly-specified models suggest that incorporating all possible information -- modeling uncertainty in labels -- is statistically efficient, consistency fails for ``standard'' approaches as soon as a loss to be minimized is even slightly mis-specified. Yet procedures leveraging aggregated information still converge to optimal classifiers, highlighting how incorporating a fuller view of the data analysis pipeline, from collection to model-fitting to prediction time, can yield a more robust methodology by refining noisy signals.

Problem

• Robustness of learning with aggregated labels
• Risk consistency for surrogate loss minimization
• Convergence to optimal classifiers despite misspecification

Innovation

• Label aggregation for robust learning
• Stronger consistency with surrogate loss
• Convergence to optimal classifiers