Shifted Composition III: Local Error Framework for KL Divergence
Jason M. Altschuler, Sinho Chewi

TL;DR
This paper introduces a novel local error framework for KL divergence that extends coupling arguments beyond Wasserstein metrics, providing tight bounds and new guarantees for sampling algorithms under various conditions.
Contribution
It develops a unified local error analysis framework for KL divergence applicable to Langevin-based sampling, surpassing previous Wasserstein-only bounds and covering broader settings.
Findings
Yields optimal rates in strongly log-concave and LSI cases
First to provide KL guarantees beyond Wasserstein in SLC
First to establish results in WLC and LSI metrics
Abstract
Coupling arguments are a central tool for bounding the deviation between two stochastic processes, but traditionally have been limited to Wasserstein metrics. In this paper, we apply the shifted composition rule--an information-theoretic principle introduced in our earlier work--in order to adapt coupling arguments to the Kullback-Leibler (KL) divergence. Our framework combine the strengths of two previously disparate approaches: local error analysis and Girsanov's theorem. Akin to the former, it yields tight bounds by incorporating the so-called weak error, and is user-friendly in that it only requires easily verified local assumptions; and akin to the latter, it yields KL divergence guarantees and applies beyond Wasserstein contractivity. We apply this framework to the problem of sampling from a target distribution . Here, the two stochastic processes are the Langevin diffusion…
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Taxonomy
TopicsAlgorithms and Data Compression
MethodsDiffusion
