  Domain generalization involves learning a classifier from a heterogeneous
collection of training sources such that it generalizes to data drawn from
similar unknown target domains, with applications in large-scale learning and
personalized inference. In many settings, privacy concerns prohibit obtaining
domain labels for the training data samples, and instead only have an
aggregated collection of training points. Existing approaches that utilize
domain labels to create domain-invariant feature representations are
inapplicable in this setting, requiring alternative approaches to learn
generalizable classifiers. In this paper, we propose a domain-adaptive approach
to this problem, which operates in two steps: (a) we cluster training data
within a carefully chosen feature space to create pseudo-domains, and (b) using
these pseudo-domains we learn a domain-adaptive classifier that makes
predictions using information about both the input and the pseudo-domain it
belongs to. Our approach achieves state-of-the-art performance on a variety of
domain generalization benchmarks without using domain labels whatsoever.
Furthermore, we provide novel theoretical guarantees on domain generalization
using cluster information. Our approach is amenable to ensemble-based methods
and provides substantial gains even on large-scale benchmark datasets. The code
can be found at: https://github.com/xavierohan/AdaClust_DomainBed
