2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
Victor Bouvier, Philippe Very, Clément Chastagnol +2
Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source domain, where labelled data are available, and a target domain only represented with unlabelled data. I…
Robust Domain Adaptation: Representations, Weights and Inductive Bias
Victor Bouvier, Philippe Very, Clément Chastagnol +2
Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years. The emergence of Domain Invariant Representations (IR) has improved drastically the tra…
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets
Victor Bouvier, Philippe Very, Céline Hudelot +1
Learning representations which remain invariant to a nuisance factor has a great interest in Domain Adaptation, Transfer Learning, and Fair Machine Learning. Finding such represent…
Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation
Victor Bouvier, Philippe Very, Céline Hudelot +1
Unsupervised Domain Adaptation aims to learn a model on a source domain with labeled data in order to perform well on unlabeled data of a target domain. Current approaches focus on…