7 citations · 8 across the 3 of their papers we have counts for
4 papers
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…
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…
Ordinal Rating of Network Performance and Inference by Matrix Completion
Wei Du, Yongjun Liao, and Pierre Geurts +1
This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matri…