13 citations · 35 across the 8 of their papers we have counts for
8 papers
Matching of Everyday Power Supply and Demand with Dynamic Pricing: Problem Formalisation and Conceptual Analysis
Thibaut Théate, Antonio Sutera, Damien Ernst
The energy transition is expected to significantly increase the share of renewable energy sources whose production is intermittent in the electricity mix. Apart from key benefits,…
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…
Simple connectome inference from partial correlation statistics in calcium imaging
Antonio Sutera, Arnaud Joly, Vincent François-Lavet +4
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, proces…