Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
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发表时间：
2021-06
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通讯作者：
Bahar Taşkesen;Man-Chung Yue;J. Blanchet;D. Kuhn;Viet Anh Nguyen
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作者：
Bahar Taşkesen;Man-Chung Yue;J. Blanchet;D. Kuhn;Viet Anh Nguyen
当对一些目标域样本进行训练时，最小二乘估计器的预测效果可能很差。监督域适应旨在通过利用来自接近目标分布的源分布的附加标记训练样本来提高预测准确性。根据现有数据，我们研究了新的策略来综合一系列在矩条件方面具有鲁棒性的最小二乘估计专家。当使用 Kullback-Leibler 或 Wasserstein 型散度指定这些矩条件时，我们可以使用凸优化有效地找到鲁棒估计量。我们在所提出的稳健专家系列上使用伯恩斯坦在线聚合算法来生成目标测试样本的顺序流的预测。对真实数据的数值实验表明，稳健策略可能优于经验最小二乘估计量的非稳健插值。
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.