Reproduce “On the Relevance-Complexity Region of Scalable Information Bottleneck” on OpenResearch
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About this paper
Mohammad Mahdi Mahvari, Mari Kobayashi, Abdellatif Zaidi
This research extends the Information Bottleneck principle to a scalable, multi-stage setting, formally characterizing the relevance-complexity region for systems that generate progressively richer data descriptions under logarithmic loss. The work provides explicit analytical solutions for memoryless binary and Gaussian sources, develops a computational algorithm for general discrete sources, and applies the framework to pattern classification.
Key insights
- The relevance-complexity region for scalable IB is defined by cumulative compression complexity and progressive relevance, bounded by mutual information terms related to successive descriptions.
- Explicit analytical solutions for memoryless binary and scalar Gaussian sources reveal a hierarchical structure of relevance, where information about the source progressively increases with richer descriptions.
- The scalable IB framework offers a principled method for designing multi-resolution learning architectures and connecting information-theoretic measures to classification performance in sequential tasks.
Summary by alphaXiv.
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