domain-adaptation

Validating assumptions on domain relationships is not possible without target labels. We argue that, in order to make domain-adaptive classifiers more practical, it is necessary to focus on robustness; robust in the sense that an adaptive classifier will still perform at least as well as a non-adaptive classifier without having to rely on the validity of strong assumptions.

Sequential Domain Adaptive Intelligent Systems focuses on ordered domain-adaptative classification over a sequence of biased samples from an underlying population.

I presented my paper on problems with importance-weighted cross-validation under covariate shift. Under covariate shift, the standard cross-validation estimator is not consistent (i.e. it won’t return optimal hyperparameter estimates). Importance-weighting the cross-validation estimator was deemed to resolve this issue, but we show that it is still not consistent.

SNN organized a one day symposium entitled Intelligent Machines, where an overview of recent developments was presented. The meeting aimed to establish a dialogue and to build connections between academic research, industry and public institutions in the Netherlands. I presented my preliminary work on incorporating transfer models in domain-adaptive classifiers.