The paper characterizes the worst-case query complexity of sampling from smooth non-log-concave distributions as exponential in dimension, with matching lower and upper bounds.
Nearly d-linear convergence bounds for diffusion models via stochastic localization
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On the query complexity of sampling from non-log-concave distributions
The paper characterizes the worst-case query complexity of sampling from smooth non-log-concave distributions as exponential in dimension, with matching lower and upper bounds.