  Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an
unlabeled domain by transferring knowledge from a separate labeled source
domain. However, most of these conventional UDA approaches make the strong
assumption of having access to the source data during training, which may not
be very practical due to privacy, security and storage concerns. A recent line
of work addressed this problem and proposed an algorithm that transfers
knowledge to the unlabeled target domain from a single source model without
requiring access to the source data. However, for adaptation purposes, if there
are multiple trained source models available to choose from, this method has to
go through adapting each and every model individually, to check for the best
source. Thus, we ask the question: can we find the optimal combination of
source models, with no source data and without target labels, whose performance
is no worse than the single best source? To answer this, we propose a novel and
efficient algorithm which automatically combines the source models with
suitable weights in such a way that it performs at least as good as the best
source model. We provide intuitive theoretical insights to justify our claim.
Furthermore, extensive experiments are conducted on several benchmark datasets
to show the effectiveness of our algorithm, where in most cases, our method not
only reaches best source accuracy but also outperforms it.
