82 citations · 196 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
Gradient tree boosting with random output projections for multi-label classification and multi-output regression
Arnaud Joly, Louis Wehenkel, Pierre Geurts
In many applications of supervised learning, multiple classification or regression outputs have to be predicted jointly. We consider several extensions of gradient boosting to addr…
Deep Quality-Value (DQV) Learning
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning. DQV uses temporal-difference learning to train a Value neural network and…
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…
Random forests with random projections of the output space for high dimensional multi-label classification
Arnaud Joly, Pierre Geurts, Louis Wehenkel
We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning…