Supervised Learning as Lossy Compression: Characterizing Generalization and Sample Complexity via Finite Blocklength Analysis
arXiv:2602.04107v2 Announce Type: replace Abstract: This paper presents a novel information-theoretic perspective on generalization in machine learning by framing the learning problem within the context of lossy compression and applying finite blocklength analysis. In our approach, the sampling of training data formally corresponds to an encoding process, and the model construction to a decoding process. By leveraging finite blocklength analysis, we derive lower bounds on sample complexity and generalization error for a fixed randomized learning algorithm and its associated optimal sampling st
This paper, published on arXiv, builds on contemporary research in information theory and machine learning, applying finite blocklength analysis to derive new theoretical bounds.
It provides a foundational, information-theoretic framework for understanding generalization and sample complexity, which are critical challenges in scaling and deploying AI systems.
The theoretical understanding of how AI models learn and generalize is refined, potentially leading to more efficient model design and data utilization.
- · AI researchers
- · Machine learning platform providers
- · Organizations with limited data
- · Heuristic model development
- · Inefficient AI training practices
Improved theoretical understanding of AI generalization properties.
Development of more data-efficient and robust machine learning algorithms.
Reduced compute and data requirements for training complex AI models, impacting compute supply chains and energy consumption.
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Read at arXiv cs.LG