学习加权表示以实现设计间的泛化
Learning Weighted Representations for Generalization Across Designs
摘要：
在分布偏移下泛化良好的预测模型通常是可取的，有时对于构建鲁棒和可靠的机器学习应用程序至关重要。我们专注于从观测数据的因果推断和无监督域适应中产生的分布转移。我们把这两个问题作为设计转变下的预测。克服分布偏移的流行方法做出了不切实际的假设，例如具有明确的模型或知道产生观察数据的政策。其他方法受到阻碍，他们需要一个预先指定的度量比较观察，或由穷人的渐近性质。我们设计了一个设计转移下的泛化误差的界，结合了表示学习和样本重新加权。基于上界，我们提出了一个算法框架，不需要任何上述假设，这是渐近一致的。我们使用两个合成数据集对新框架进行了实证研究，并与以前的方法相比证明了其有效性。
英文摘要：
Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to building robust and reliable machine learning applications. We focus on distributional shift that arises in causal inference from observational data and in unsupervised domain adaptation. We pose both of these problems as prediction under a shift in design. Popular methods for overcoming distributional shift make unrealistic assumptions such as having a well-specified model or knowing the policy that gave rise to the observed data. Other methods are hindered by their need for a pre-specified metric for comparing observations, or by poor asymptotic properties. We devise a bound on the generalization error under design shift, incorporating both representation learning and sample re-weighting. Based on the bound, we propose an algorithmic framework that does not require any of the above assumptions and which is asymptotically consistent. We empirically study the new framework using two synthetic datasets, and demonstrate its effectiveness compared to previous methods.
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