Optimal Transport and Machine Learning

Over the last decade, optimal transport (OT) has evolved from a prize-winning research area in pure mathematics to a recurring theme bursting across many areas of machine learning (ML). Advancements in OT theory, computation, and statistics have fueled breakthroughs in a wide range of applications, from single-cell genomics \cite{schiebinger2019optimal} to generative modeling \cite{arjovsky2017wasserstein} and optimization of over-parametrized neural nets \cite{chizat2018global,de2021diffusion}, among many others. The OTML workshop series (in '14,~'17,~'19, and '21) has been instrumental in shaping this influential research thread. For~this new OTML installment, we aim even higher by hosting two exceptional plenary speakers: Luis Caffarelli, who received the 2023 Abel Prize for his seminal contributions to regularity theory for the Monge–Amp{`e}re equation and OT, and Felix Otto, the 2006 Leibniz Prize awardee and 2017 Blaise Pascal medalist, who made profound contributions to the theory of Wasserstein gradient flows. The OTML workshop will provide a unique platform to federate, disseminate, and advance current knowledge in this rapidly growing field. This, in turn, will facilitate cross-field fertilization and drive the community towards future groundbreaking discoveries.