The Conditional Entropy Bottleneck

Area of Science:

• Machine Learning
• Artificial Intelligence
• Robust Generalization

Background:

• Machine learning models exhibit failure modes like adversarial vulnerability, poor out-of-distribution detection, miscalibration, and memorization of random labels.
• These failures are characterized as a lack of robust generalization, extending beyond traditional accuracy metrics.
• A key hypothesis is that these failures stem from models retaining excessive information about training data.

Purpose of the Study:

• To introduce the Minimum Necessary Information (MNI) criterion for evaluating model quality.
• To propose a new objective function, the Conditional Entropy Bottleneck (CEB), to train models adhering to the MNI criterion.
• To empirically validate the hypothesis that minimizing information retention enhances robust generalization.

Main Methods:

• Development of the Minimum Necessary Information (MNI) criterion.
• Introduction of the Conditional Entropy Bottleneck (CEB) objective function, related to the Information Bottleneck (IB).
• Experimental comparison of CEB models against deterministic and Variational Information Bottleneck (VIB) models on various datasets and robustness challenges.

Main Results:

• CEB models trained with the MNI criterion demonstrated improved performance on robustness challenges.
• Empirical evidence supports the hypothesis that limiting information retention enhances model generalization.
• The proposed CEB objective function effectively guides models towards the MNI criterion.

Conclusions:

• Retaining minimal necessary information is crucial for achieving robust generalization in machine learning.
• The Conditional Entropy Bottleneck (CEB) offers a viable approach to train models that satisfy the Minimum Necessary Information (MNI) criterion.
• This work provides a new perspective and methodology for addressing fundamental limitations in current machine learning models.