112 citations · 162 across the 9 of their papers we have counts for
10 papers
Improving the generalizability and robustness of large-scale traffic signal control
Tianyu Shi, Francois-Xavier Devailly, Denis Larocque +1
A number of deep reinforcement-learning (RL) approaches propose to control traffic signals. In this work, we study the robustness of such methods along two axes. First, sensor fail…
Covariance regression with random forests
Cansu Alakus, Denis Larocque, Aurelie Labbe
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience…
Model-based graph reinforcement learning for inductive traffic signal control
François-Xavier Devailly, Denis Larocque, Laurent Charlin
Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection or after any modification to the road ne…
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests
Cansu Alakus, Denis Larocque, Aurelie Labbe
Like many predictive models, random forests provide point predictions for new observations. Besides the point prediction, it is important to quantify the uncertainty in the predict…
Dynamic estimation with random forests for discrete-time survival data
Hoora Moradian, Weichi Yao, Denis Larocque +2
Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we in…
Conditional canonical correlation estimation based on covariates with random forests
Cansu Alakus, Denis Larocque, Sebastien Jacquemont +5
Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correla…