Domain adaptation

…neutrino and/or collider physics; domain adaptation and model robustness; uncertainty quantification; and model interpretability. Experience with simulation-based inference with normalizing flows or flow matching, diffusion models, transformers or

Optimal transport supports clustering, dictionary learning and domain adaptation, but high-dimensional data often force projections that discard information. Charles Bouveyron develops an alternative for Gaussian distributions with parsimonious covariance structure and low intrinsic dimension. A low-dimensional decomposition of empirical covariance matrices gives an efficient, robust closed form for the 2-Wasserstein distance without projecting the data. An explicit Monge map uses exact covariance square roots and inverse square roots. Numerical comparisons demonstrate analytical and computati