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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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p x∈R y∈Rp where F is the outer objective and f is the inner objective. Solving such problems is challenging due to the need to compute gradients through
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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) models. Little attention has been paid to other models like graph NNs (GNNs) or PCA. Among the few existing works in the literature, [2] proposes an FL algorithm to compute PCA in a DP fashion, but the
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revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria
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on Neural Information Processing System [20] Anish Agarwal, Munther Dahleh, and Tuhin Sarkar, A marketplace for data: An algorithmic solution, in Proceedings of the 2019 ACM Conference on Economics and
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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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. The monitoring of telecommunications and energy production and distribution networks are characteristic examples of such time-critical applications. The project aims to propose unsupervised online CPD algorithms