Improving the generalizability and robustness of large-scale traffic signal control

no code implementations • 2 Jun 2023 • Tianyu Shi, Francois-Xavier Devailly, Denis Larocque, Laurent Charlin

Building upon the state-of-the-art previous model which uses a decentralized approach for large-scale traffic signal control with graph convolutional networks (GCNs), we first learn models using a distributional reinforcement learning (DisRL) approach.

Distributional Reinforcement Learning Multi-agent Reinforcement Learning +2

Covariance regression with random forests

1 code implementation • 16 Sep 2022 • 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, epidemiology and biomedicine.

Epidemiology regression

Model-based graph reinforcement learning for inductive traffic signal control

1 code implementation • 1 Aug 2022 • 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 network, traffic distribution, or behavioral constraints experienced during training.

reinforcement-learning Reinforcement Learning (RL)

RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

2 code implementations • 15 Jun 2021 • Cansu Alakus, Denis Larocque, Aurelie Labbe

The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020).

Prediction Intervals

Dynamic estimation with random forests for discrete-time survival data

1 code implementation • 1 Mar 2021 • Hoora Moradian, Weichi Yao, Denis Larocque, Jeffrey S. Simonoff, Halina Frydman

Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available.

Methodology Applications

Conditional canonical correlation estimation based on covariates with random forests

2 code implementations • 23 Nov 2020 • Cansu Alakus, Denis Larocque, Sebastien Jacquemont, Fanny Barlaam, Charles-Olivier Martin, Kristian Agbogba, Sarah Lippe, Aurelie Labbe

We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates.

EEG

Ensemble methods for survival function estimation with time-varying covariates

2 code implementations • 31 May 2020 • Weichi Yao, Halina Frydman, Denis Larocque, Jeffrey S. Simonoff

We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated L2 difference between the true and estimated survival functions.

IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

1 code implementation • 6 Mar 2020 • François-Xavier Devailly, Denis Larocque, Laurent Charlin

We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines.

Multi-agent Reinforcement Learning reinforcement-learning +1