Stable Prediction across Unknown Environments
未知环境间的稳定预测AI 解读 ↓
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未知环境间的稳定预测
- 研究问题
- 训练时不知道未来测试分布，且训练与测试环境可能发生分布变化时，如何学习能够跨未知环境保持稳定的分类预测？
- 方法与思路
- 作者提出 DGBR，联合优化深度自编码器与全局平衡回归，以降维便利权重构造，并估计控制其他特征后的偏效应。
- 材料陈述的贡献
- 摘要称理论与合成、真实数据实验支持跨环境稳定预测，并报告优于所比较方法；具体性能数值和适用范围未给出。
- 阅读关注点
- 应核对稳定预测的环境变化范围、平衡权重与表示学习目标，以及把特征关系解释为因果关系所需的识别条件。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未核实；摘要未列理论条件和实验细节，不能据此确认普遍优势或因果识别。
原文摘要
In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting the training data with the ratio of the density between test and training data. In many applications training takes place without prior knowledge of the testing distribution on which the algorithm will be applied in the future. Recently, methods have been proposed to address the shift by learning causal structure, but those methods rely on the diversity of multiple training data to a good performance, and have complexity limitations in high dimensions. In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments. The global balancing model constructs balancing weights that facilitate estimating of partial effects of features (holding fixed all other features), a problem that is challenging in high dimensions, and thus helps to identify stable, causal relationships between features and outcomes. The deep auto-encoder model is designed to reduce the dimensionality of the feature space, thus making global balancing easier. We show, both theoretically and with empirical experiments, that our algorithm can make stable predictions across unknown environments. Our experiments on both synthetic and real world datasets demonstrate that our DGBR algorithm outperforms the state-of-the-art methods for stable prediction across unknown environments.