ARROW enables variance reduction for domain adaptation on streaming data by adaptively reweighting minibatches, making it practical for online and distributed learning scenarios where offline algorithms fail.
This paper introduces ARROW, an online algorithm for reducing variance in domain adaptation tasks using streaming data. Unlike existing offline methods, ARROW works with data arriving continuously by maintaining moving averages of alignment statistics and reweighting incoming batches to match reference statistics, enabling efficient learning in real-time settings.