2 papers
cs.LG2021
Unsupervised domain adaptation with non-stochastic missing data
Matthieu Kirchmeyer, Patrick Gallinari, Alain Rakotomamonjy +1
We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data in the unlabelled target domain. More precisely, motivated by practical…
cs.LG2019
Benchmarking Regression Methods: A comparison with CGAN
Karan Aggarwal, Matthieu Kirchmeyer, Pranjul Yadav +2
In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (…