Distributionally Robust Graphical Models
Distributionally Robust Graphical Models
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发表时间：
2018-11
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通讯作者：
Rizal Fathony;Ashkan Rezaei;Mohammad Ali Bashiri;Xinhua Zhang;Brian D. Ziebart
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作者：
Rizal Fathony;Ashkan Rezaei;Mohammad Ali Bashiri;Xinhua Zhang;Brian D. Ziebart
在许多结构化预测问题中，变量之间的复杂关系是用图形结构紧凑地定义的。最流行的图形预测方法——概率图形模型和大边际方法——有其独特的优势，但也有明显的缺点。条件随机场（CRFs）是Fisher一致的，但它们不允许将定制的损失指标集成到它们的学习过程中。大利润模型，如结构化支持向量机（ssvm），可以灵活地纳入定制的损失指标，但缺乏Fisher一致性保证。我们提出了对抗图形模型（AGM），这是一种分布鲁棒的方法，用于构建预测器，该预测器对使用图形结构定义的一类数据分布具有鲁棒性。我们的方法既具有将定制损失指标纳入其设计的灵活性，又具有Fisher一致性的统计保证。我们提出了时间复杂度与现有图形模型相似的AGM精确学习和预测算法，并通过实验展示了我们方法的实际好处。
In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---probabilistic graphical models and large margin methods---have their own distinct strengths but also possess significant drawbacks. Conditional random fields (CRFs) are Fisher consistent, but they do not permit integration of customized loss metrics into their learning process. Large-margin models, such as structured support vector machines (SSVMs), have the flexibility to incorporate customized loss metrics, but lack Fisher consistency guarantees. We present adversarial graphical models (AGM), a distributionally robust approach for constructing a predictor that performs robustly for a class of data distributions defined using a graphical structure. Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency. We present exact learning and prediction algorithms for AGM with time complexity similar to existing graphical models and show the practical benefits of our approach with experiments.