Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Least squares estimators, when trained on a 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 may outperform non-robust interpolations of the empirical least squares estimators.
Code (1)
Tasks
Domain AdaptationSimilar Papers 제목 키워드 기반
Distributionally Robust Domain Adaptation
Domain Adaptation (DA) has recently received significant attention due to its potential to adapt a learning model across source and target domains with mismatched distributions. Since DA methods rely exclusively on the g…
Domain AdaptationDistributionally Robust Policy and Lyapunov-Certificate Learning
This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. A key challenge in designing controllers with stab…
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Empirical risk minimization often performs poorly when the distribution of the target domain differs from those of source domains. To address such potential distribution shifts, we develop an unsupervised domain adaptati…
Domain AdaptationFederated LearningMulti-Source Unsupervised Domain AdaptationPrediction+1Distributionally Robust Learning for Unsupervised Domain Adaptation
We propose a distributionally robust learning (DRL) method for unsupervised domain adaptation (UDA) that scales to modern computer-vision benchmarks. DRL can be naturally formulated as a competitive two-player game be…
Density Ratio EstimationDomain AdaptationUnsupervised Domain AdaptationDistributionally Robust Classification for Multi-source Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) is a statistical learning problem when the distribution of training (source) data is different from that of test (target) data. In this setting, one has access to labeled data only fr…
Unsupervised Domain Adaptation