  Comparing two probability measures supported on heterogeneous spaces is an
increasingly important problem in machine learning. Such problems arise when
comparing for instance two populations of biological cells, each described with
its own set of features, or when looking at families of word embeddings trained
across different corpora/languages. For such settings, the Gromov Wasserstein
(GW) distance is often presented as the gold standard. GW is intuitive, as it
quantifies whether one measure can be isomorphically mapped to the other.
However, its exact computation is intractable, and most algorithms that claim
to approximate it remain expensive. Building on \cite{memoli-2011}, who
proposed to represent each point in each distribution as the 1D distribution of
its distances to all other points, we introduce in this paper the Anchor Energy
(AE) and Anchor Wasserstein (AW) distances, which are respectively the energy
and Wasserstein distances instantiated on such representations. Our main
contribution is to propose a sweep line algorithm to compute AE \emph{exactly}
in log-quadratic time, where a naive implementation would be cubic. This is
quasi-linear w.r.t. the description of the problem itself. Our second
contribution is the proposal of robust variants of AE and AW that uses rank
statistics rather than the original distances. We show that AE and AW perform
well in various experimental settings at a fraction of the computational cost
of popular GW approximations. Code is available at
\url{https://github.com/joisino/anchor-energy}.
