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Sergei Pereverzyev

2 accepted papers

2023

Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation

ICLR 2023top-5%

We study the problem of choosing algorithm hyper-parameters in unsupervised domain adaptation, i.e., with labeled data in a source domain and unlabeled data in a target domain, drawn from a different input distribution. We follow the strategy to compute several models using different hyper-parameter…

2021

The balancing principle for parameter choice in distance-regularized domain adaptation

NeurIPS 2021poster

We address the unsolved algorithm design problem of choosing a justified regularization parameter in unsupervised domain adaptation. This problem is intriguing as no labels are available in the target domain. Our approach starts with the observation that the widely-used method of minimizing the sour…