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minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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motivated the development of Federated Learning (FL) [1,2], a framework for on-device collaborative training of machine learning models. FL algorithms like FedAvg [3] allow clients to train a common global
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. Indeed, when multiple sources exist in the vicinity of a same sensing unit, their signatures mix and estimation of individual sources is disturbed by the other co-occurring sources. The aim of the doctoral
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analyzed. The tensor model structure estimated by suitable optimization algorithms, such as that recently developed in [GOU20], will be considered as a starting point. • Exploiting data multimodality and
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national Luxembourg AI Strategy featuring a healthcare flagship A transnational network of competence that includes multiple Max-Planck and Helmholtz institutes in Saarbrücken The opportunity to impact