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Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise

arXiv:2604.06468v1 Announce Type: new Abstract: Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extractors, resources typically unavailable when robustness is most needed. We propose Conformal Margin Risk Minimization (CMRM), a plug-and-play envelope framework that improves any classification loss under label noise by adding a single quantile-calibrated regularization term, with no privileged knowledge or training pipeline modification. CMRM measures the confidence margin between the observed label and competing labels, and thresholds it with a conformal quantile estimated per batch to focus training on high-margin samples while suppressing likely mislabeled ones. We derive a learning bound for CMRM under arbitrary label noise requiring only mild regularity of the margin distribution. Across five base methods and six benchmarks with synthetic and real-world noise, CMRM consistently improves

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Yuanjie Shi, Peihong Li, Zijian Zhang, Janardhan Rao Doppa, Yan Yan · · 1 min read · 38 views
#cs.LG #stat.ML

arXiv:2604.06468v1 Announce Type: new Abstract: Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extractors, resources typically unavailable when robustness is most needed. We propose Conformal Margin Risk Minimization (CMRM), a plug-and-play envelope framework that improves any classification loss under label noise by adding a single quantile-calibrated regularization term, with no privileged knowledge or training pipeline modification. CMRM measures the confidence margin between the observed label and competing labels, and thresholds it with a conformal quantile estimated per batch to focus training on high-margin samples while suppressing likely mislabeled ones. We derive a learning bound for CMRM under arbitrary label noise requiring only mild regularity of the margin distribution. Across five base methods and six benchmarks with synthetic and real-world noise, CMRM consistently improves accuracy (up to +3.39%), reduces conformal prediction set size (up to -20.44%) and does not hurt under 0% noise, showing that CMRM captures a method-agnostic uncertainty signal that existing mechanisms did not exploit.

Executive Summary

The article introduces Conformal Margin Risk Minimization (CMRM), a novel, plug-and-play envelope framework designed to enhance robust learning in the presence of label noise. Unlike many existing methods, CMRM operates without requiring privileged knowledge such as noise transition matrices or clean subsets, making it particularly valuable when such resources are scarce. It achieves this by adding a quantile-calibrated regularization term to any classification loss, focusing training on high-margin samples and suppressing potentially mislabeled ones based on a per-batch conformal quantile estimation. The framework demonstrates consistent improvements in accuracy and reduced conformal prediction set sizes across diverse benchmarks and base methods, even under varying noise levels, without detriment under zero noise. Its method-agnostic uncertainty signal represents a significant step forward in label noise robustness.

Key Points

  • ▸ CMRM is a plug-and-play envelope framework that improves classification loss under label noise without requiring privileged knowledge.
  • ▸ It operates by adding a single quantile-calibrated regularization term, thresholding a confidence margin with a per-batch conformal quantile.
  • ▸ The method focuses training on high-margin samples while suppressing likely mislabeled ones, capturing a method-agnostic uncertainty signal.
  • ▸ A learning bound for CMRM is derived, requiring only mild regularity of the margin distribution under arbitrary label noise.
  • ▸ Empirical results show consistent improvements in accuracy (up to +3.39%) and reduced conformal prediction set size (up to -20.44%), with no performance degradation under 0% noise.

Merits

Knowledge-Free Robustness

CMRM's primary strength is its independence from privileged knowledge (e.g., noise matrices, clean subsets), making it highly practical for real-world scenarios where such information is often unavailable.

Plug-and-Play Universality

The framework can be applied to 'any classification loss' and 'five base methods,' demonstrating its versatility and ease of integration into existing learning pipelines without modification.

Method-Agnostic Uncertainty Signal

By exploiting a confidence margin and conformal quantile, CMRM captures a universal uncertainty signal that enhances robustness across diverse models, indicating a fundamental insight into label noise.

Empirical Efficacy and Consistency

Consistent improvements in accuracy and prediction set size across six benchmarks, synthetic and real-world noise, and multiple base methods underscore its robust performance.

Theoretical Foundation

The derivation of a learning bound under arbitrary label noise provides a rigorous theoretical underpinning, strengthening the framework's credibility beyond empirical results.

Demerits

Computational Overhead

While 'plug-and-play,' the addition of a 'single quantile-calibrated regularization term' and per-batch quantile estimation might introduce a marginal computational overhead, though the article does not extensively quantify this.

Sensitivity to Margin Distribution Regularity

The learning bound 'requiring only mild regularity of the margin distribution' implies a potential vulnerability if this regularity assumption is significantly violated in certain complex datasets or noise patterns.

Interpretability of Quantile Estimation

The 'conformal quantile estimated per batch' is central; its stability and interpretability across highly dynamic or imbalanced batches could warrant further investigation.

Expert Commentary

The CMRM framework represents a significant advancement in the field of robust learning, particularly for its elegant solution to the perennial problem of label noise without demanding privileged information. Its 'plug-and-play' nature and demonstrated universality across diverse methods and benchmarks are highly commendable, suggesting a foundational insight into how models can intrinsically discern reliable signals amidst noise. The emphasis on a 'method-agnostic uncertainty signal' is particularly compelling, as it implies a generalizable principle rather than an architecture-specific hack. The theoretical grounding, despite its 'mild regularity' assumption, provides a crucial intellectual anchor. Future research should delve into the framework's behavior under extreme noise distributions, the computational efficiency of the per-batch quantile estimation at scale, and its potential synergy with other robustness techniques like active learning or self-supervised pre-training. This work has strong potential to influence practical deployment strategies for AI in real-world, imperfect data environments.

Recommendations

  • ✓ Conduct a detailed analysis of the computational overhead introduced by CMRM, especially for very large datasets and complex models, to ensure scalability.
  • ✓ Explore the sensitivity of CMRM's performance to violations of the 'mild regularity of the margin distribution' assumption under diverse and adversarial noise patterns.
  • ✓ Investigate the interaction of CMRM with other data augmentation and regularization techniques to understand potential synergistic or conflicting effects.
  • ✓ Develop tools or visualizations to better interpret the 'conformal quantile estimated per batch' and its dynamic behavior during training.
  • ✓ Extend empirical evaluations to include a wider range of real-world noisy datasets and domain-specific applications (e.g., medical imaging, legal document classification) to further validate generalizability.

Sources

Original: arXiv - cs.LG

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