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Tian-Yi Wu

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#machine learning Preprint Oct 2026

KL Before and After Mixing: A Round-Complexity Separation in Parallel Sampling

A parallel sampler can choose at random which coordinates to generate together. For a target $p$ and the output law $q_U$ of a schedule $U$ drawn afresh for each output, we compare the schedule-averaged divergence $\mathbb{E}_U D_{\mathrm{KL}}(p\|q_U)$ with the output-law divergence $D_{\mathrm{KL}}(p\|\mathbb{E}_U q_U...

Tian-Yi Wu · 3 citations

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