no code implementations • 13 Feb 2024 • Florian Beier, Hancheng Bi, Clément Sarrazin, Bernhard Schmitzer, Gabriele Steidl
In this paper, we are concerned with estimating the joint probability of random variables $X$ and $Y$, given $N$ independent observation blocks $(\boldsymbol{x}^i,\boldsymbol{y}^i)$, $i=1,\ldots, N$, each of $M$ samples $(\boldsymbol{x}^i,\boldsymbol{y}^i) = \bigl((x^i_j, y^i_{\sigma^i(j)}) \bigr)_{j=1}^M$, where $\sigma^i$ denotes an unknown permutation of i. i. d.
no code implementations • NeurIPS 2021 • Quentin Merigot, Filippo Santambrogio, Clément Sarrazin
A common way to do so relies on the construction of a uniform probability distribution over a set of $N$ points which minimizes the Wasserstein distance to the model distribution.
