1. Semi-supervised siscriminative classification robust to sample-outliers and feature-noises;Adeli;IEEE Transactions on Pattern Analysis and Machine Intelligence,2019
2. Distribution-matching embedding for visual domain adaptation;Baktashmotlagh;Journal of Machine Learning Research,2016
3. Manifold regularization: a geometric framework for learning from labeled and unlabele examples;Belkin;Journal of Machine Learning Research,2006
4. Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proc. association for computational linguistics (pp. 440–447).
5. Blitzer, J., McDonald, R. T., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. In Proc. 2006 conference on empirical methods in natural language processing (pp. 120–128).