<ul><li>Gaussian mixture models (GMMs) are widely used in machine learning for various tasks.</li><li>A key challenge in working with GMMs is defining a computationally efficient and geometrically meaningful metric.</li><li>The mixture Wasserstein (MW) distance has been applied in various domains, but its high computational cost limits scalability to high-dimensional and large-scale problems.</li><li>To address this, the researchers propose slicing-based approximations to the MW distance that reduce computational complexity while preserving optimal transport properties.</li></ul>

Slicing the Gaussian Mixture Wasserstein Distance

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