21 citations · 60 across the 15 of their papers we have counts for
16 papers
Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining
Théophile Berteloot, Richard Khoury, Audrey Durand
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to highly explainable classification syste…
GrowSpace: Learning How to Shape Plants
Yasmeen Hitti, Ionelia Buzatu, Manuel Del Verme +3
Plants are dynamic systems that are integral to our existence and survival. Plants face environment changes and adapt over time to their surrounding conditions. We argue that plant…
ACReL: Adversarial Conditional value-at-risk Reinforcement Learning
M. Godbout, M. Heuillet, S. Chandra +2
In the classical Reinforcement Learning (RL) setting, one aims to find a policy that maximizes its expected return. This objective may be inappropriate in safety-critical domains s…
Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data
M. Godbout, A. Lachance, F. Antaki +2
We investigate the potential of machine learning models for the prediction of visual improvement after macular hole surgery from preoperative data (retinal images and clinical feat…
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments
Joseph Jay Williams, Jacob Nogas, Nina Deliu +4
Multi-armed bandit algorithms have been argued for decades as useful for adaptively randomized experiments. In such experiments, an algorithm varies which arms (e.g. alternative in…
Deep interpretability for GWAS
Deepak Sharma, Audrey Durand, Marc-André Legault +4
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In these studies, association testing is done…