We introduce COT-GAN, an adversarial algorithm to train implicit generative models
optimized for producing sequential data. The loss function of this algorithm is formulated …

This paper studies the convergence problem for mean field games with common noise. We
define a suitable notion of weak mean field equilibria, which we prove captures all …

Adapted optimal transport (AOT) problems are optimal transport problems for distributions of
a time series where couplings are constrained to have a temporal causal structure. In this …

This work presents a distributionally robust Kalman filter to address uncertainties in noise
covariance matrices and predicted covariance estimates. We adopt a distributionally robust …

We develop a fitted value iteration (FVI) method to compute bicausal optimal transport (OT)
where couplings have an adapted structure. Based on the dynamic programming …

This paper considers a variational inequality (VI) problem arising from a game among
multiple agents, where each agent aims to minimize its own cost function subject to its …

We establish the existence and uniqueness of the equilibrium for a stochastic mean-field
game of optimal investment. The analysis covers both finite and infinite time horizons, and …

In this paper, we provide a quantitative analysis of the concept of arbitrage, that allows us to
deal with model uncertainty without imposing the no‐arbitrage condition. In markets that …

In this paper we introduce a variant of optimal transport adapted to the causal structure given
by an underlying directed graph. Different graph structures lead to different specifications of …

We consider a large population dynamic game in discrete time where players are
characterized by time-evolving types. It is a natural assumption that the players' actions …