Inference via robust optimal transportation: theory and methods

arXiv:2301.06297v4 Announce Type: replace-cross
Abstract: Optimal transportation theory and the related $p$-Wasserstein distance ($W_p$, $p\geq 1$) are widely-applied in statistics and machine learning. In spite of their popularity, inference based on these tools has some issues. For instance, it is sensitive to outliers and it may not be even defined when the underlying model has infinite moments. To cope with these problems, first we consider a robust version of the primal transportation problem and show that it defines the {robust …

abstract arxiv inference instance machine machine learning math.st outliers robust statistics stat.ml stat.th theory tools transportation type via