A tensorized stochastic localization process with non-Gaussian tilts yields explicit spectral gap bounds and rapid mixing for tensor Ising models.
Polynomials and multi linear mappings in topological vector-spaces
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Stochastic Localization with Non-Gaussian Tilts and Applications to Tensor Ising Models
A tensorized stochastic localization process with non-Gaussian tilts yields explicit spectral gap bounds and rapid mixing for tensor Ising models.