3 papers
cs.AI2026
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Nazim Bendib, Nicolas Perrin-Gilbert, Olivier Sigaud
Offline zero-shot reinforcement learning (RL) aims to learn agents that optimize unseen reward functions without additional environment interaction. The standard approach to this p…
cs.LG2025
A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler
Mohammed Tirichine, Nassim Ameur, Nazim Bendib +4
Code optimization is a crucial task that aims to enhance code performance. However, this process is often tedious and complex, highlighting the necessity for automatic code optimiz…
cs.CV2024
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning
Nazim Bendib
Data augmentation plays a critical role in generating high-quality positive and negative pairs necessary for effective contrastive learning. However, common practices involve using…