  Learning guarantees often rely on assumptions of i.i.d. data, which will
likely be violated in practice once predictors are deployed to perform
real-world tasks. Domain adaptation approaches thus appeared as a useful
framework yielding extra flexibility in that distinct train and test data
distributions are supported, provided that other assumptions are satisfied such
as covariate shift, which expects the conditional distributions over labels to
be independent of the underlying data distribution. Several approaches were
introduced in order to induce generalization across varying train and test data
sources, and those often rely on the general idea of domain-invariance, in such
a way that the data-generating distributions are to be disregarded by the
prediction model. In this contribution, we tackle the problem of generalizing
across data sources by approaching it from the opposite direction: we consider
a conditional modeling approach in which predictions, in addition to being
dependent on the input data, use information relative to the underlying
data-generating distribution. For instance, the model has an explicit mechanism
to adapt to changing environments and/or new data sources. We argue that such
an approach is more generally applicable than current domain adaptation methods
since it does not require extra assumptions such as covariate shift and further
yields simpler training algorithms that avoid a common source of training
instabilities caused by minimax formulations, often employed in
domain-invariant methods.
