82 citations · 196 across the 22 of their papers we have counts for
10 papers · 1 filter
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…
Parallelizing Autoregressive Generation with Variational State Space Models
Gaspard Lambrechts, Yann Claes, Pierre Geurts +1
Attention-based models such as Transformers and recurrent models like state space models (SSMs) have emerged as successful methods for autoregressive sequence modeling. Although bo…
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…
Distillation from heterogeneous unlabeled collections
Jean-Michel Begon, Pierre Geurts
Compressing deep networks is essential to expand their range of applications to constrained settings. The need for compression however often arises long after the model was trained…
On The Transferability of Deep-Q Networks
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, rangi…
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…
Evaluating Local Explanations using White-box Models
Amir Hossein Akhavan Rahnama, Judith Butepage, Pierre Geurts +1
Evaluating explanation techniques using human subjects is costly, time-consuming and can lead to subjectivity in the assessments. To evaluate the accuracy of local explanations, we…
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
Pascal Leroy, Damien Ernst, Pierre Geurts +3
This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings. All algorithms are bas…
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly…
Classifying pairs with trees for supervised biological network inference
Marie Schrynemackers, Louis Wehenkel, M. Madan Babu +1
Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used t…