Title: Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts Show Chinese title

Title: 通过合成分布稳健专家的顺序域适应

Abstract: 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. Show Chinese abstract

Abstract: 最小二乘估计量在仅基于少量目标域样本训练时，可能预测效果不佳。监督领域适应旨在通过利用来自接近目标分布的源分布的额外标记训练样本来提高预测准确性。在给定的数据条件下，我们研究了新的策略来合成一组对矩条件具有鲁棒性的最小二乘估计专家。当这些矩条件使用 Kullback-Leibler 或 Wasserstein 类型的散度指定时，我们可以高效地通过凸优化找到鲁棒估计器。我们使用伯恩斯坦在线聚合算法在所提出的鲁棒专家族上生成对目标测试样本序列流的预测。实证数值实验表明，鲁棒策略可能优于经验最小二乘估计器的非鲁棒插值方法。