Efficient Convex PCA with applications to Wasserstein geodesic PCA and ranked data

We contribute to the theoretical and computational development of Convex PCA, and present some applications of independent interest in SPT.

Deep learning for principal-agent mean field games

In this paper we develop a deep learning algorithm for solving Principal-Agent (PA) mean field games with market-clearing conditions.