4 citations · 9 across the 10 of their papers we have counts for
7 papers · 1 filter
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
Julius von Kügelgen, Alexander Mey, Marco Loog +1
While the success of semi-supervised learning (SSL) is still not fully understood, Schölkopf et al. (2012) have established a link to the principle of independent causal mechanisms…
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Julius von Kügelgen, Alexander Mey, Marco Loog
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 onl…
Single Shot Active Learning using Pseudo Annotators
Yazhou Yang, Marco Loog
Standard myopic active learning assumes that human annotations are always obtainable whenever new samples are selected. This, however, is unrealistic in many real-world application…
Effects of sampling skewness of the importance-weighted risk estimator on model selection
Wouter M. Kouw, Marco Loog
Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has desirable characteristics such as unbiasedness, c…
Active Learning Using Uncertainty Information
Yazhou Yang, Marco Loog
Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classi…
Projected Estimators for Robust Semi-supervised Classification
Jesse H. Krijthe, Marco Loog
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification usin…