Salem Lahlou

According to our database, Salem Lahlou authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

On Generalization for Generative Flow Networks.

Anas Krichel, Nikolay Malkin, Salem Lahlou, Yoshua Bengio

CoRR, 2024

PORT: Preference Optimization on Reasoning Traces.

Salem Lahlou, Abdalgader Abubaker, Hakim Hacid

CoRR, 2024

Investigating Regularization of Self-Play Language Models.

Réda Alami, Abdalgader Abubaker, Mastane Achab, Mohamed El Amine Seddik, Salem Lahlou

CoRR, 2024

2023

DEUP: Direct Epistemic Uncertainty Prediction.

Salem Lahlou, Moksh Jain, Hadi Nekoei, Victor Butoi, Paul Bertin, Jarrid Rector-Brooks, Maksym Korablyov, Yoshua Bengio

Trans. Mach. Learn. Res., 2023

GFlowNet Foundations.

Yoshua Bengio, Salem Lahlou, Tristan Deleu, Edward J. Hu, Mo Tiwari, Emmanuel Bengio

J. Mach. Learn. Res., 2023

BatchGFN: Generative Flow Networks for Batch Active Learning.

Shreshth A. Malik, Salem Lahlou, Andrew Jesson, Moksh Jain, Nikolay Malkin, Tristan Deleu, Yoshua Bengio, Yarin Gal

CoRR, 2023

Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.

Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo de Lazcano, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, Jordan K. Terry

CoRR, 2023

torchgfn: A PyTorch GFlowNet library.

Salem Lahlou, Joseph D. Viviano, Victor Schmidt

CoRR, 2023

Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.

Maxime Chevalier-Boisvert, Bolun Dai, Mark Towers, Rodrigo Perez-Vicente, Lucas Willems, Salem Lahlou, Suman Pal, Pablo Samuel Castro, Jordan K. Terry

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

GFlowOut: Dropout with Generative Flow Networks.

Dianbo Liu, Moksh Jain, Bonaventure F. P. Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Chinenye Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2023

A theory of continuous generative flow networks.

Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin

Proceedings of the International Conference on Machine Learning, 2023

GFlowNets and variational inference.

Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2020

Mastering Rate based Curriculum Learning.

Lucas Willems, Salem Lahlou, Yoshua Bengio

CoRR, 2020

2019

BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning.

Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop.

Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio

CoRR, 2018