112 citations · 117 across the 9 of their papers we have counts for
4 papers · 1 filter
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
Emiliano Penaloza, Tianyue H. Zhang, Laurent Charlin +1
Concept Bottleneck Models (CBMs) propose to enhance the trustworthiness of AI systems by constraining their decisions on a set of human-understandable concepts. However, CBMs typic…
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…
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…
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
François-Xavier Devailly, Denis Larocque, Laurent Charlin
Scaling adaptive traffic-signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning attempts to address this challenge by distri…