Nathan Grinsztajn

According to our database, Nathan Grinsztajn authored at least 21 papers between 2020 and 2024.

Collaborative distances:

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

2024

Averaging log-likelihoods in direct alignment.

Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar, Florian Strub, Bill Wu, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Olivier Pietquin, Matthieu Geist

CoRR, 2024

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion.

Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist

CoRR, 2024

Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization.

Félix Chalumeau, Refiloe Shabe, Noah de Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn

CoRR, 2024

Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs.

Andries P. Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D. Barrett, Arnu Pretorius

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX.

Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Sasha Abramowitz, Paul Duckworth, Vincent Coyette, Laurence Illing Midgley, Elshadai Tegegn, Tristan Kalloniatis, Omayma Mahjoub, Matthew Macfarlane, Andries P. Smit, Nathan Grinsztajn, Raphaël Boige, Cemlyn N. Waters, Mohamed A. Mimouni, Ulrich A. Mbou Sob, Ruan de Kock, Siddarth Singh, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, Alexandre Laterre

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Reinforcement learning for combinatorial optimization : leveraging uncertainty, structure and priors. (Apprentissage par renforcement pour l'optimisation combinatoire : exploiter l'incertitude, les structures et les connaissances a priori).

Nathan Grinsztajn

PhD thesis, 2023

Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A.

Andries P. Smit, Paul Duckworth, Nathan Grinsztajn, Kale-ab Tessera, Thomas D. Barrett, Arnu Pretorius

CoRR, 2023

Combinatorial Optimization with Policy Adaptation using Latent Space Search.

Félix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Thomas D. Barrett

CoRR, 2023

Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX.

Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Vincent Coyette, Paul Duckworth, Laurence I. Midgley, Tristan Kalloniatis, Sasha Abramowitz, Cemlyn N. Waters, Andries P. Smit, Nathan Grinsztajn, Ulrich A. Mbou Sob, Omayma Mahjoub, Elshadai Tegegn, Mohamed A. Mimouni, Raphaël Boige, Ruan de Kock, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, Alexandre Laterre

CoRR, 2023

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization.

Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Clément Bonnet, Tom Barrett

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

Combinatorial Optimization with Policy Adaptation using Latent Space Search.

Félix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Tom Barrett

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

2022

Population-Based Reinforcement Learning for Combinatorial Optimization.

Nathan Grinsztajn, Daniel Furelos-Blanco, Thomas D. Barrett

CoRR, 2022

Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round.

Manh Hung Nguyen, Lisheng Sun, Nathan Grinsztajn, Isabelle Guyon

CoRR, 2022

Meta-learning from Learning Curves: Challenge Design and Baseline Results.

Manh Hung Nguyen, Lisheng Sun-Hosoya, Nathan Grinsztajn, Isabelle Guyon

Proceedings of the International Joint Conference on Neural Networks, 2022

2021

More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences.

Toby Johnstone, Nathan Grinsztajn, Johan Ferret, Philippe Preux

CoRR, 2021

Low-Rank Projections of GCNs Laplacian.

Nathan Grinsztajn, Philippe Preux, Edouard Oyallon

CoRR, 2021

Interferometric Graph Transform for Community Labeling.

Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon

CoRR, 2021

MetaREVEAL: RL-based Meta-learning from Learning Curves.

Manh Hung Nguyen, Isabelle Guyon, Lisheng Sun-Hosoya, Nathan Grinsztajn

Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2021) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), 2021

There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning.

Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

READYS: A Reinforcement Learning Based Strategy for Heterogeneous Dynamic Scheduling.

Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux

Proceedings of the IEEE International Conference on Cluster Computing, 2021

2020

Geometric deep reinforcement learning for dynamic DAG scheduling.

Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux

Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020