  Domain adaptation addresses the common problem when the target distribution
generating our test data drifts from the source (training) distribution. While
absent assumptions, domain adaptation is impossible, strict conditions, e.g.
covariate or label shift, enable principled algorithms. Recently-proposed
domain-adversarial approaches consist of aligning source and target encodings,
often motivating this approach as minimizing two (of three) terms in a
theoretical bound on target error. Unfortunately, this minimization can cause
arbitrary increases in the third term, e.g. they can break down under shifting
label distributions. We propose asymmetrically-relaxed distribution alignment,
a new approach that overcomes some limitations of standard domain-adversarial
algorithms. Moreover, we characterize precise assumptions under which our
algorithm is theoretically principled and demonstrate empirical benefits on
both synthetic and real datasets.
