Tighter Information-Theoretic Generalization Bounds via a Novel Class of Change of Measure Inequalities
arXiv:2602.07999v4 Announce Type: replace-cross Abstract: Change of measure inequalities translate divergences between probability measures into explicit bounds on event probabilities, and play an important role in deriving probabilistic guarantees in learning theory, information theory, and statistics. We propose novel change of measure inequalities via a unified framework based on the data processing inequality, which is surprisingly elementary yet powerful enough to yield novel, tighter inequalities. We provide change of measure inequalities in terms of a broad family of information measure
This research provides fundamental theoretical advancements in information theory, a core component of machine learning and artificial intelligence, building on existing computational methods.
Improved generalization bounds directly enhance the reliability and efficiency of AI models, leading to more robust and trustworthy applications in diverse fields.
The theoretical underpinnings for understanding and guaranteeing AI model performance are strengthened, enabling the development of more stable and predictable AI systems.
- · AI researchers and developers
- · Companies using AI for critical applications
- · Fields requiring high-assurance AI systems
- · Developers relying on heuristic generalizations
- · AI systems with opaque generalization properties
More rigorous methods for evaluating the generalization performance of machine learning models will emerge.
This foundational work could accelerate the development of explainable and verifiable AI systems across various industries.
Increased trust in AI due to provable guarantees might lead to broader adoption in highly regulated and sensitive sectors.
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Read at arXiv cs.LG