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New research separates KL divergence criteria in parallel sampling algorithms

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This paper explores the theoretical complexities of parallel sampling algorithms, specifically comparing two criteria for evaluating sampling schedules: schedule-averaged KL divergence and output-law KL divergence. The research introduces a round-complexity separation, demonstrating that under certain conditions, the output-law divergence can be significantly lower than the schedule-averaged divergence. This separation is analyzed for various targets, including uniform distributions on binary strings and Beta(1,1)-Bernoulli distributions, with findings indicating that specific round counts can be achieved with rare, long runs. AI

RANK_REASON The item is an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=0.1]

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New research separates KL divergence criteria in parallel sampling algorithms

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tianyi Wu ·

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

    arXiv:2610.13924v1 Announce Type: new Abstract: 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_{\…

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