5 citations · 13 across the 6 of their papers we have counts for
8 papers
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…
A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series
Gautier Marti, Philippe Very, Philippe Donnat +1
We present in this paper an empirical framework motivated by the practitioner point of view on stability. The goal is to both assess clustering validity and yield market insights b…
HCMapper: An interactive visualization tool to compare partition-based flat clustering extracted from pairs of dendrograms
Gautier Marti, Philippe Donnat, Frank Nielsen +1
We describe a new visualization tool, dubbed HCMapper, that visually helps to compare a pair of dendrograms computed on the same dataset by displaying multiscale partition-based la…
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…
Toward a generic representation of random variables for machine learning
Gautier Marti, Philippe Very, Philippe Donnat
This paper presents a pre-processing and a distance which improve the performance of machine learning algorithms working on independent and identically distributed stochastic proce…