It is a common idea that high dimensional data (or features) may lie on low dimensional support making learning easier. In this talk, I will present a very general set-up in which it is possible to recover low dimensional non-linear structures with noisy data, the noise being totally unknown and possibly large.
Then I will present minimax rates for the estimation of the support in Hausdorff distance.
This presentation will focus on hybrid AI, as a step towards explainability, more specifically in the domain of spatial reasoning and image understanding. Image understanding benefits from the modeling of knowledge about both the scene observed and the objects it contains as well as their relationships. We show in this context the contribution of hybrid artificial intelligence, combining...
In this presentation, I will present some results on optimization in the context of federated learning with compression. I will first summarise the main challenges and the type of results the community has obtained, and dive into some more recent results on tradeoffs between convergence and compression rates, and user-heterogeneity. In particular, I will describe two fundamental phenomenons...
In recent years, considerable progress has been made in the implementation of decision support procedures based on machine learning methods through the exploitation of very large databases and the use of learning algorithms.
In the industrial environment, the databases available in research and development or in production are rarely so voluminous and the question arises as to whether in...
In this talk, we focus on covariance matrix inference and principal component analysis in the context of non-regular data under heterogeneous environments. First, we briefly introduce mixed effects models, which are widely used to analyze repeated measures data arising in several signal processing applications that need to incorporate the same global individual's behavior with possible local...
Transfer learning has become increasingly important in recent years, particularly because learning a new model for each task can be much more costly in terms of training examples than adapting a model learned for another task. The standard approach in neural networks is to reuse the learned representation in the first layers and to adapt the decision function performed by the last layers.
In...