Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only. Here, we consider a particular case of covariate shift which allows us also to learn from unlabelled data, that is, combining adaptation with semi-supervised learning. Using ideas from causality, we argue that this requires learning with both causes, $X_C$, and effects, $X_E$, of a target variable, $Y$, and show how this setting leads to what we call a semi-generative model, $P(Y,X_E|X_C,\theta)$. Our approach is robust to domain shifts in the distribution of causal features and leverages unlabelled data by learning a direct map from causes to effects. Experiments on synthetic data demonstrate significant improvements in classification over purely-supervised and importance-weighting baselines.
Code (1)
Tasks
Domain AdaptationSimilar Papers 제목 키워드 기반
Estimating Conditional Average Treatment Effects with Missing Treatment Information
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has rec…
Domain AdaptationLeveraging unsupervised data and domain adaptation for deep regression in low-cost sensor calibration
Air quality monitoring is becoming an essential task with rising awareness about air quality. Low cost air quality sensors are easy to deploy but are not as reliable as the costly and bulky reference monitors. The low qu…
Domain AdaptationregressionSemi-supervised Domain AdaptationOptimal Policy Adaptation under Covariate Shift
Transfer learning of prediction models has been extensively studied, while the corresponding policy learning approaches are rarely discussed. In this paper, we propose principled approaches for learning the optimal polic…
Transfer LearningEffective Sample Size, Dimensionality, and Generalization in Covariate Shift Adaptation
In supervised learning, training and test datasets are often sampled from distinct distributions. Domain adaptation techniques are thus required. Covariate shift adaptation yields good generalization performance when dom…
Dimensionality ReductionDomain Adaptationfeature selectionGeneralization BoundsDomain Adaptation under Missingness Shift
Rates of missing data often depend on record-keeping policies and thus may change across times and locations, even when the underlying features are comparatively stable. In this paper, we introduce the problem of Domain …
Domain Adaptation