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Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features

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If you have a question about this talk, please contact Adrian Weller .

In this talk, I will present a novel, causally-inspired approach to domain adaptation which aims to also include unlabelled data in the model fitting when labelled data is scarce. We consider a case of covariate-shift adaptation with cause and effect features, and–drawing from recent ideas in causal modelling and invariant prediction—show how this setting leads to, what we will refer to as, a semi-generative model: P(Y, X_eff |X_cau, θ). Our proposed approach is robust to changes in the distribution over causal features, and naturally allows to impose model constraints by unsupervised learning of a map from causes to effects. In experiments on synthetic datasets we demonstrate a significant improvement in classification performance of our semi-generative model over purely-supervised and importance-weighting baselines when the amount of labelled data is small. Moreover, we apply our approach for regression on real-world protein-count data and compare it to feature transformation methods.

Speaker bio: Julius von Kugelgen will be joining MLG as a new PhD student later this year, as part of the Cambridge-Tuebingen programme.

This talk is part of the Machine Learning @ CUED series.

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