3 citations · 3 across the 7 of their papers we have counts for
8 papers
Learning the subspace of variation for global optimization of functions with low effective dimension
Coralia Cartis, Xinzhu Liang, Estelle Massart +1
We propose an algorithmic framework, that employs active subspace techniques, for scalable global optimization of functions with low effective dimension (also referred to as low-ra…
Coordinate descent on the orthogonal group for recurrent neural network training
Estelle Massart, Vinayak Abrol
We propose to use stochastic Riemannian coordinate descent on the orthogonal group for recurrent neural network training. The algorithm rotates successively two columns of the recu…
Global optimization using random embeddings
Coralia Cartis, Estelle Massart, Adilet Otemissov
We propose a random-subspace algorithmic framework for global optimization of Lipschitz-continuous objectives, and analyse its convergence using novel tools from conic integral geo…
Constrained global optimization of functions with low effective dimensionality using multiple random embeddings
Coralia Cartis, Estelle Massart, Adilet Otemissov
We consider the bound-constrained global optimization of functions with low effective dimensionality, that are constant along an (unknown) linear subspace and only vary over the ef…
Low-rank multi-parametric covariance identification
Antoni Musolas, Estelle Massart, Julien M. Hendrickx +2
We propose a differential geometric construction for families of low-rank covariance matrices, via interpolation on low-rank matrix manifolds. In contrast with standard parametric…
Balanced truncation for parametric linear systems using interpolation of Gramians: a comparison of algebraic and geometric approaches
Nguyen Thanh Son, Pierre-Yves Gousenbourger, Estelle Massart +1
When balanced truncation is used for model order reduction, one has to solve a pair of Lyapunov equations for two Gramians and uses them to construct a reduced-order model. Althoug…