Sliced Wasserstein Sketches with Error Certificates
Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions. We study optimal, free-weight $M$-atom quantization of one-dimensional probability measures in quadratic Wasserstein distance and extend it to higher dimensions via slicing. Using $L$ shar...
Elaheh Akbari, James Liu, Keaton Hamm et al.
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