  To train machine learning models that are robust to distribution shifts in
the data, distributionally robust optimization (DRO) has been proven very
effective. However, the existing approaches to learning a distributionally
robust model either require solving complex optimization problems such as
semidefinite programming or a first-order method whose convergence scales
linearly with the number of data samples -- which hinders their scalability to
large datasets. In this paper, we show how different variants of DRO are simply
instances of a finite-sum composite optimization for which we provide scalable
methods. We also provide empirical results that demonstrate the effectiveness
of our proposed algorithm with respect to the prior art in order to learn
robust models from very large datasets.
