3 citations · 10 across the 13 of their papers we have counts for
13 papers
A Training-Free Regeneration Paradigm: Contrastive Reflection Memory Guided Self-Verification and Self-Improvement
Yuran Li, Di Wu, Benoit Boulet
Verification-guided self-improvement has recently emerged as a promising approach to improving the accuracy of large language model (LLM) outputs. However, existing approaches face…
Causal Feature Selection for Weather-Driven Residential Load Forecasting
Elise Zhang, François Mirallès, Stéphane Dellacherie +2
Weather is a dominant external driver of residential electricity demand, but adding many meteorological covariates can inflate model complexity and may even impair accuracy. Select…
Leveraging LLMs as Meta-Judges: A Multi-Agent Framework for Evaluating LLM Judgments
Yuran Li, Jama Hussein Mohamud, Chongren Sun +2
Large language models (LLMs) are being widely applied across various fields, but as tasks become more complex, evaluating their responses is increasingly challenging. Compared to h…
MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems
Elise Zhang, François Mirallès, Raphaël Rousseau-Rizzi +3
Convergent Cross Mapping (CCM) is a powerful method for detecting causality in coupled nonlinear dynamical systems, providing a model-free approach to capture dynamic causal intera…
OnionEval: An Unified Evaluation of Fact-conflicting Hallucination for Small-Large Language Models
Chongren Sun, Yuran Li, Di Wu +1
Large Language Models (LLMs) are highly capable but require significant computational resources for both training and inference. Within the LLM family, smaller models (those with f…
DRDT3: Diffusion-Refined Decision Test-Time Training Model
Xingshuai Huang, Di Wu, Benoit Boulet
Decision Transformer (DT), a trajectory modelling method, has shown competitive performance compared to traditional offline reinforcement learning (RL) approaches on various classi…
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning
Xingshuai Huang, Di Wu, Benoit Boulet
Offline reinforcement learning (RL) enables policy learning from pre-collected offline datasets, relaxing the need to interact directly with the environment. However, limited by th…
Robot Policy Learning with Temporal Optimal Transport Reward
Yuwei Fu, Haichao Zhang, Di Wu +2
Reward specification is one of the most tricky problems in Reinforcement Learning, which usually requires tedious hand engineering in practice. One promising approach to tackle thi…
Trajectory Prediction for Autonomous Driving using Agent-Interaction Graph Embedding
Jilan Samiuddin, Benoit Boulet, Di Wu
Trajectory prediction module in an autonomous driving system is crucial for the decision-making and safety of the autonomous agent car and its surroundings. This work presents a no…
An Online Self-learning Graph-based Lateral Controller for Self-Driving Cars
Jilan Samiuddin, Benoit Boulet, Di Wu
The hype around self-driving cars has been growing over the past years and has sparked much research. Several modules in self-driving cars are thoroughly investigated to ensure saf…
FuRL: Visual-Language Models as Fuzzy Rewards for Reinforcement Learning
Yuwei Fu, Haichao Zhang, Di Wu +2
In this work, we investigate how to leverage pre-trained visual-language models (VLM) for online Reinforcement Learning (RL). In particular, we focus on sparse reward tasks with pr…
An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles
Jilan Samiuddin, Benoit Boulet, Di Wu
The autonomous driving industry is expected to grow by over 20 times in the coming decade and, thus, motivate researchers to delve into it. The primary focus of their research is t…
Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach
Xingshuai Huang, Di Wu, Benoit Boulet
Efficient traffic signal control is critical for reducing traffic congestion and improving overall transportation efficiency. The dynamic nature of traffic flow has prompted resear…