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 | 22 | 2021 |
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 | 22 | 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 | 20* | 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 | 19* | 2022 |
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 | 11 | 2023 |
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 | 8 | 2020 |
Jumanji: a diverse suite of scalable reinforcement learning environments in jax, 2023 C Bonnet, D Luo, D Byrne, S Surana, V Coyette, P Duckworth, LI Midgley, ... URL https://arxiv. org/abs/2306.09884, 0 | 7 | |
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 | 6 | 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 | 5 | 2022 |
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 | 3 | 2022 |
Low-rank projections of GCNs laplacian N Grinsztajn, P Preux, E Oyallon ICLR 2021 Workshop on Geometrical and Topological Representation Learning, 2021 | 3 | 2021 |
Interferometric graph transform for community labeling N Grinsztajn, L Leconte, P Preux, E Oyallon arXiv preprint arXiv:2106.05875, 2021 | 3 | 2021 |
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 | 2 | 2023 |
Better state exploration using action sequence equivalence N Grinsztajn, T Johnstone, J Ferret, P Preux NeurIPS 2022-Deep Reinforcement Learning Workshop, 2022 | 2* | 2022 |
Averaging log-likelihoods in direct alignment N Grinsztajn, Y Flet-Berliac, MG Azar, F Strub, B Wu, E Choi, C Cremer, ... arXiv preprint arXiv:2406.19188, 2024 | | 2024 |
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion Y Flet-Berliac, N Grinsztajn, F Strub, E Choi, C Cremer, A Ahmadian, ... arXiv preprint arXiv:2406.19185, 2024 | | 2024 |
Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization F Chalumeau, R Shabe, N de Nicola, A Pretorius, TD Barrett, N Grinsztajn arXiv preprint arXiv:2406.16424, 2024 | | 2024 |
Apprentissage par renforcement pour l'optimisation combinatoire: exploiter l'incertitude, les structures et les connaissances a priori N Grinsztajn | | 2023 |
Reinforcement learning for combinatorial optimization: leveraging uncertainty, structure and priors N Grinsztajn Université de Lille, 2023 | | 2023 |
Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking S Surana, N Grinsztajn, T Atkinson, P Duckworth, TD Barrett ICML 2024 AI for Science Workshop, 0 | | |