By starting a Markov chain from the empirical distribution of samples, mixing time depends on a higher-order spectral gap, and sample complexity grows only linearly in the number of modes.
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Efficiently learning and sampling multimodal distributions with data-based initialization
By starting a Markov chain from the empirical distribution of samples, mixing time depends on a higher-order spectral gap, and sample complexity grows only linearly in the number of modes.