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On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances

ICML 2023 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Nonparametric and PAC-Bayes Bayesian learning · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace Euclidean Wasserstein (W_2/W_1) metric with Orlicz-Wasserstein distance using exponential convex Phi(x)=exp(x^beta)-1 (1<beta<16/15) for measuring mixing-measure contractionDirichlet process Gaussian mixture models (posterior contraction of mixing measures) · theoretical posterior contraction rateposterior contraction rate of outlier mass (excess mass)Helped
Use entropic regularized Orlicz-Wasserstein transport plans instead of entropic Euclidean Wasserstein plans (ablation)transport plan between a 3-component normal mixture and a 4-component Laplace mixture · simulation illustrationqualitative transport plan sharpness and outlier mass assignedHelped

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