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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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, the post-doctoral fellow will consider designing distributed learning algorithms for streaming manifold-valued data. Experiments will be carried out on urban, coastal, and underwater DAS data. The novelty
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. Processing this response provides estimates of the local variations in acoustic pressure along the fiber, over distances ranging from 40km up to 140km with some systems. This technique, called Distributed
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frameworks, as they impose minimal, if any, assumptions about the underlying data distribution, making them more effective for detecting a wide range of changes. The CPD algorithms will be designed
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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
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heterogeneity, i.e., the fact that clients' local datasets are in general drawn from different distributions. Statistical heterogeneity for example slows down the convergence of FL algorithms [5]. In this thesis
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learning community. Basically, once the observed data is identified with a probability distribution (possibly the empirical mass function), optimal transport allows to consistently assess the similarity
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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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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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to accelerate evaluation of costly simulations Genetic algorithms and other evolutionary techniques to generate a diverse set of high-performing solutions. You will design and implement new optimization