Exploration-Exploitation Tradeoff in Universal Lossy Compression

Nir Weinberger; Ram Zamir

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arXiv · 2506.20261

Exploration-Exploitation Tradeoff in Universal Lossy Compression

Nir Weinberger·Ram Zamir

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Abstract

Universal compression can learn the source and adapt to it either in a batch mode (forward adaptation), or in a sequential mode (backward adaptation). We recast the sequential mode as a multi-armed bandit problem, a fundamental model in reinforcement-learning, and study the trade-off between exploration and exploitation in the lossy compression case. We show that a previously proposed "natural type selection" scheme can be cast as a reconstruction-directed MAB algorithm, for sequential lossy compression, and explain its limitations in terms of robustness and short-block performance. We then derive and analyze robust cost-directed MAB algorithms, which work at any block length.

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Nir Weinberger, Ram Zamir. 2025-06-25. Exploration-Exploitation Tradeoff in Universal Lossy Compression. https://arxiv.org/abs/2506.20261

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