Multidimensional scaling is a statistical process that aims to embed high dimensional data into a lower-dimensional space; this process is often used for the purpose of data visualisation. Common multidimensional scaling algorithms tend to have high computational complexities, making them inapplicable on large data sets. This work introduces a stochastic, force directed approach to multidimensional scaling with a time and space complexity of O(N), with N data points.

This paper presents a model-agnostic ensemble approach for supervised learning. The proposed approach is based on a parametric version of Random Subspace, in which each base model is learned from a feature subset sampled according to a Bernoulli distribution. Parameter optimization is performed using gradient descent and is rendered tractable by using an importance sampling approach that circumvents frequent re-training of the base models after each gradient descent step.

Despite the many optimization algorithms available in the literature and the
several different approaches that study them, understanding the behaviour of

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Stochastic Local Search algorithms (SLS) are a class of methods used to
tackle hard combinatorial optimization problems. Despite not providing, in
most cases, any guarantee on the quality of the final solution, they are often
able to produce high quality solutions in a relatively short time. They are
therefore routinely used in countless real world applications, and their wide
applicability generates in turn a lot of research interest, both from a theo-
retical perspective and with applications on countless problems. There are

Various tasks encountered in real-world surveillance can be addressed by determining posteriors (e.g. by Bayesian inference or machine learning), based on which critical decisions must be taken. However, the surveillance domain (acquisition device, operating conditions, etc.) is often unknown, which prevents any possibility of scene-specific optimization. In this paper, we define a probabilistic framework and present a formal proof of an algorithm for the unsupervised many-to-infinity domain adaptation of posteriors.

Proposer une méthodologie basée sur le ML pour prédire les champs physiques (2D/3D)

• Prédiction de champs physiques "en temps réel"
• Fusionner des données hétérogènes (données expérimentales, données simulées de différents niveaux de fidélité)
• Hybridation de connaissance a priori / experte avec le ML via des réseaux de neurones informés (par la physique)

The main purpose of this topic is to develop, test and analyse clustering methods for residential energy consumption data to:​

• Decrease time series forecasters training time with transfer learning​
• Increase forecasting accuracy with better consumer behaviour understanding​
• Identify and interpret energy consumption patterns in the data