4 papers
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…
Comment partitionner automatiquement des marches aléatoires ? Avec application à la finance quantitative
Gautier Marti, Frank Nielsen, Philippe Very +1
We present in this paper a novel non-parametric approach useful for clustering Markov processes. We introduce a pre-processing step consisting in mapping multivariate independent a…