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Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen · Man-Chung Yue · Jose Blanchet · Daniel Kuhn · Viet Anh Nguyen

Tue Jul 20 09:00 PM -- 11:00 PM (PDT) @ Virtual #None

Least squares estimators, when trained on few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additional labeled training samples from a source distribution that is close to the target distribution. Given available data, we investigate novel strategies to synthesize a family of least squares estimator experts that are robust with regard to moment conditions. When these moment conditions are specified using Kullback-Leibler or Wasserstein-type divergences, we can find the robust estimators efficiently using convex optimization. We use the Bernstein online aggregation algorithm on the proposed family of robust experts to generate predictions for the sequential stream of target test samples. Numerical experiments on real data show that the robust strategies systematically outperform non-robust interpolations of the empirical least squares estimators.

Author Information

Bahar Taskesen (EPFL)
Man-Chung Yue (Hong Kong Polytechnic University)
Jose Blanchet (Stanford University)
Daniel Kuhn (EPFL)
Viet Anh Nguyen (Stanford University / VinAI Research)

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