Nathan Grinsztajn
Cited by
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There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning
N Grinsztajn, J Ferret, O Pietquin, P Preux, M Geist
Neurips 2021, 2021
READYS: a reinforcement learning based strategy for heterogeneous dynamic scheduling
N Grinsztajn, O Beaumont, E Jeannot, P Preux
2021 IEEE International Conference on Cluster Computing (CLUSTER), 70-81, 2021
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
N Grinsztajn, D Furelos-Blanco, S Surana, C Bonnet, T Barrett
Advances in Neural Information Processing Systems 36, 2023
Jumanji: Industry-Driven Hardware-Accelerated RL Environments.(2022)
C Bonnet, D Byrne, V Le, L Midgley, D Luo, C Waters, S Abramowitz, ...
URL https://github. com/instadeepai/jumanji, 2022
Geometric deep reinforcement learning for dynamic DAG scheduling
N Grinsztajn, O Beaumont, E Jeannot, P Preux
2020 IEEE Symposium Series on Computational Intelligence (SSCI), 258-265, 2020
Combinatorial Optimization with Policy Adaptation using Latent Space Search
F Chalumeau, S Surana, C Bonnet, N Grinsztajn, A Pretorius, A Laterre, ...
Thirty-seventh Conference on Neural Information Processing Systems, 2023
MetaREVEAL: RL-based meta-learning from learning curves
MH Nguyen, N Grinsztajn, I Guyon, L Sun-Hosoya
Workshop on Interactive Adaptive Learning co-located with European …, 2021
Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round
MH Nguyen, L Sun, N Grinsztajn, I Guyon
arXiv preprint arXiv:2208.02821, 2022
Low-rank projections of GCNs laplacian
N Grinsztajn, P Preux, E Oyallon
ICLR 2021 Workshop on Geometrical and Topological Representation Learning, 2021
Interferometric graph transform for community labeling
N Grinsztajn, L Leconte, P Preux, E Oyallon
arXiv preprint arXiv:2106.05875, 2021
Better state exploration using action sequence equivalence
N Grinsztajn, T Johnstone, J Ferret, P Preux
NeurIPS 2022-Deep Reinforcement Learning Workshop, 2022
Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A
A Smit, P Duckworth, N Grinsztajn, K Tessera, TD Barrett, A Pretorius
arXiv preprint arXiv:2311.17371, 2023
Meta-learning from Learning Curves: Challenge Design and Baseline Results
MH Nguyen, L Sun-Hosoya, N Grinsztajn, I Guyon
2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022
Apprentissage par renforcement pour l'optimisation combinatoire: exploiter l'incertitude, les structures et les connaissances a priori
N Grinsztajn
Reinforcement learning for combinatorial optimization: leveraging uncertainty, structure and priors
N Grinsztajn
Université de Lille, 2023
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