1 code implementation • Data Mining and Knowledge Discovery 2023 • Amir Hossein Akhavan Rahnama, Judith Bütepage, Pierre Geurts, Henrik Boström
Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores.
1 code implementation • 5 Jul 2023 • 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 methods combining data-driven and model-based approaches.
no code implementations • 17 Jan 2022 • Jean-Michel Begon, Pierre Geurts
Compressing deep networks is essential to expand their range of applications to constrained settings.
1 code implementation • NeurIPS 2021 • Antonio Sutera, Gilles Louppe, Van Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
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 variables to predict a certain output.
no code implementations • 6 Oct 2021 • Matthia Sabatelli, Pierre Geurts
Transfer Learning (TL) is an efficient machine learning paradigm that allows overcoming some of the hurdles that characterize the successful training of deep neural networks, ranging from long training times to the needs of large datasets.
1 code implementation • 7 Sep 2021 • Vân Anh Huynh-Thu, Pierre Geurts
The degree of randomization in our parametric Random Subspace is thus automatically tuned through the optimization of the feature selection probabilities.
no code implementations • 4 Jun 2021 • Amir Hossein Akhavan Rahnama, Judith Butepage, Pierre Geurts, Henrik Bostrom
Evaluating explanation techniques using human subjects is costly, time-consuming and can lead to subjectivity in the assessments.
1 code implementation • 22 Dec 2020 • Pascal Leroy, Damien Ernst, Pierre Geurts, Gilles Louppe, Jonathan Pisane, Matthia Sabatelli
This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings.
no code implementations • 11 May 2020 • Matthia Sabatelli, Mike Kestemont, Pierre Geurts
We study the generalization properties of pruned neural networks that are the winners of the lottery ticket hypothesis on datasets of natural images.
1 code implementation • 5 May 2020 • Romain Mormont, Pierre Geurts, Raphaël Marée
In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology.
3 code implementations • 1 Sep 2019 • Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms.
no code implementations • 18 May 2019 • Arnaud Joly, Louis Wehenkel, Pierre Geurts
We consider several extensions of gradient boosting to address such problems.
3 code implementations • 30 Sep 2018 • Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning.
no code implementations • 4 Sep 2017 • Antonio Sutera, Célia Châtel, Gilles Louppe, Louis Wehenkel, Pierre Geurts
Dealing with datasets of very high dimension is a major challenge in machine learning.
1 code implementation • ICML 2017 • Jean-Michel Begon, Arnaud Joly, Pierre Geurts
Tree-based ensemble models are heavy memory-wise.
no code implementations • 12 May 2016 • Antonio Sutera, Gilles Louppe, Vân Anh Huynh-Thu, Louis Wehenkel, Pierre Geurts
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output.
1 code implementation • 30 Jun 2014 • Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data.
no code implementations • 24 Apr 2014 • Marie Schrynemackers, Louis Wehenkel, M. Madan Babu, Pierre Geurts
Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference.
no code implementations • 14 Apr 2014 • 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.
no code implementations • NeurIPS 2013 • Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view.