Unsupervised Domain Adaptation with Non-stochastic Missing Data | Amin Mantrach | Criteo

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Presenter: Amin Mantrach

Title: Unsupervised Domain Adaptation with Non-stochastic Missing Data

Date: 2020-07-23

Abstract: We consider unsupervised domain adaptation (UDA) for classification problems, in the presence of missing data in the target domain. More precisely, we focus on the case where there is a domain shift between source and target and where some components are systematically absent on target samples. This setting, motivated by practical applications, has not yet been considered to our knowledge. We propose a way to impute such non-stochastic missing data in a latent space by leveraging supervision from a complete source domain through UDA. We introduce a single model performing joint adaptation, imputation and classification which is shown to perform well under various representative divergence families (H-divergence, Optimal Transport). This model is further improved via a self-training procedure that helps bringing the learned source and target class posterior distributions closer together. We perform experiments on two families of datasets: a classical digit classification benchmark commonly used in UDA and real world digital advertising datasets and analyze the model’s behavior showing the benefits of jointly performing imputation with adaptation. Work conducted with my teammates Matthieu Kirchmeyer, Alain Rakotomamonjy and Patrick Gallinari.

Short bio: Amin Mantrach is Staff Research Scientist at Criteo AI Lab where he leads a research team with the objective to transfer knowledge between different business advertising domains using machine learning techniques. His team’s research falls in the domain of representation learning using deep unsupervised approaches as auto-regressive models, flows, and variational auto-encoders. Before that Amin was Research Scientist for years at Yahoo Sunnyvale, Barcelona, and Xerox France.

What Is Stochastic Divergence, Unsupervised Domain Adaptation with Non-stochastic Missing Data | Amin Mantrach | Criteo.

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