3 citations · 6 across the 4 of their papers we have counts for
6 papers
From global to local MDI variable importances for random forests and when they are Shapley values
Antonio Sutera, Gilles Louppe, Van Anh Huynh-Thu +2
Random forests have been widely used for their ability to provide so-called importance measures, which give insight at a global (per dataset) level on the relevance of input variab…
Importance measures derived from random forests: characterisation and extension
Antonio Sutera
Nowadays new technologies, and especially artificial intelligence, are more and more established in our society. Big data analysis and machine learning, two sub-fields of artificia…
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
Jonathan Dumas, Antoine Wehenkel Damien Lanaspeze, Bertrand Cornélusse +1
Greater direct electrification of end-use sectors with a higher share of renewables is one of the pillars to power a carbon-neutral society by 2050. However, in contrast to convent…
A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended version
Jonathan Dumas, Colin Cointe, Antoine Wehenkel +3
This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to p…
Random Subspace with Trees for Feature Selection Under Memory Constraints
Antonio Sutera, Célia Châtel, Gilles Louppe +2
Dealing with datasets of very high dimension is a major challenge in machine learning. In this paper, we consider the problem of feature selection in applications where the memory…
Context-dependent feature analysis with random forests
Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu +2
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions tha…