针对先验概率漂移，直接在无标签数据上优化模型超参数。
Transductive Model Selection under Prior Probability Shift
- 在无标签数据上直接优化超参数，无需依赖有标签数据。
- 实验表明该方法在先验概率漂移场景下显著提升模型性能。
- 适合反因果学习等存在数据分布偏移的任务场景。
转导学习是一种监督学习任务，与传统归纳学习不同，其待标注的无标签数据是有限集合且在训练时可用。类似归纳学习，转导学习也可能受到数据集漂移的影响，即不满足独立同分布假设。本文提出一种专用于转导分类的模型选择方法，用于处理先验概率漂移——一种典型的反因果学习问题中的数据集漂移类型。所提方法可直接在需部署分类器的无标签数据上优化超参数，区别于传统基于有标签训练数据交叉验证的方法。实验结果表明该方法具有明显优势。
原文摘要 · Abstract (English)
Transductive learning is a supervised machine learning task in which, unlike in traditional inductive learning, the unlabelled data that require labelling are a finite set and are available at training time. Similarly to inductive learning contexts, transductive learning contexts may be affected by dataset shift, i.e., may be such that the IID assumption does not hold. We here propose a method, tailored to transductive classification contexts, for performing model selection (i.e., hyperparameter optimisation) when the data exhibit prior probability shift, an important type of dataset shift typical of anti-causal learning problems. In our proposed method the hyperparameters can be optimised directly on the unlabelled data to which the trained classifier must be applied; this is unlike traditional model selection methods, that are based on performing cross-validation on the labelled training data. We provide experimental results that show the benefits brought about by our method.
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