3 citations · 5 across the 6 of their papers we have counts for
7 papers
Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Yann Claes, Pierre Geurts, Vân Anh Huynh-Thu
Over the last few years, there has been an increased interest in making machine learning models more interpretable. Although a great deal of effort goes into developing techniques…
Hybrid additive modeling with partial dependence for supervised regression and dynamical systems forecasting
Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts
Learning processes by exploiting restricted domain knowledge is an important task across a plethora of scientific areas, with more and more hybrid training methods additively combi…
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…
Optimizing model-agnostic Random Subspace ensembles
Vân Anh Huynh-Thu, Pierre Geurts
This paper presents a model-agnostic ensemble approach for supervised learning. The proposed approach is based on a parametric version of Random Subspace, in which each base model…
Gene regulatory network inference: an introductory survey
Vân Anh Huynh-Thu, Guido Sanguinetti
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing th…
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…
Bridging physiological and evolutionary time scales in a gene regulatory network
Gwenaëlle Marchand, Vân Anh Huynh-Thu, Nolan Kane +9
Gene regulatory networks (GRN) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time scale…