Nathan Grinsztajn

27 papers A* 6B 1C 1Journal 16Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking. CoRR Shikha Surana, Nathan Grinsztajn, Timothy Atkinson, Paul Duckworth, Thomas D. Barrett
2025	J	`jnl`	ShiQ: Bringing back Bellman to LLMs. CoRR Pierre Clavier, Nathan Grinsztajn, Raphaël Avalos, Yannis Flet-Berliac, Irem Ergün, Omar Darwiche Domingues, Eugene Tarassov, Olivier Pietquin, Pierre H. Richemond, Florian Strub, Matthieu Geist
2024	J	`jnl`	Averaging log-likelihoods in direct alignment. CoRR Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar, Florian Strub, Bill Wu, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Olivier Pietquin, Matthieu Geist
2024	J	`jnl`	Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier. CoRR John Dang, Shivalika Singh, Daniel D'souza, Arash Ahmadian, Alejandro Salamanca, Madeline Smith, Aidan Peppin, Sungjin Hong, Manoj Govindassamy, Terrence Zhao, Sandra Kublik, Meor Amer, Viraat Aryabumi, Jon Ander Campos, Yi Chern Tan, Tom Kocmi, Florian Strub, Nathan Grinsztajn, Yannis Flet-Berliac, Acyr Locatelli, Hangyu Lin, Dwarak Talupuru, Bharat Venkitesh, David Cairuz, Bowen Yang, Tim Chung, Wei-Yin Ko, Sylvie Shang Shi, Amir Shukayev, Sammie Bae, Aleksandra Piktus, Roman Castagné, Felipe Cruz-Salinas, Eddie Kim, Lucas Crawhall-Stein, Adrien Morisot, Sudip Roy, Phil Blunsom, Ivan Zhang, Aidan N. Gomez, Nick Frosst, Marzieh Fadaee, Beyza Ermis, Ahmet Üstün, Sara Hooker
2024	A*	`conf`	Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. EMNLP Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Bill Wu, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist
2024	J	`jnl`	Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. CoRR Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist
2024	A*	`conf`	Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. ICLR 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
2024	J	`jnl`	Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization. CoRR Félix Chalumeau, Refiloe Shabe, Noah de Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn
2024	A*	`conf`	Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs. ICML Andries P. Smit, Nathan Grinsztajn, Paul Duckworth, Thomas D. Barrett, Arnu Pretorius
2023	J	`jnl`	Are we going MAD? Benchmarking Multi-Agent Debate between Language Models for Medical Q&A. CoRR Andries P. Smit, Paul Duckworth, Nathan Grinsztajn, Kale-ab Abebe Tessera, Thomas D. Barrett, Arnu Pretorius
2023	A*	`conf`	Combinatorial Optimization with Policy Adaptation using Latent Space Search. NeurIPS Félix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Tom Barrett
2023	J	`jnl`	Combinatorial Optimization with Policy Adaptation using Latent Space Search. CoRR Félix Chalumeau, Shikha Surana, Clément Bonnet, Nathan Grinsztajn, Arnu Pretorius, Alexandre Laterre, Thomas D. Barrett
2023	J	`jnl`	Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX. CoRR 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
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
2023	A*	`conf`	Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization. NeurIPS Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Clément Bonnet, Tom Barrett
2022	J	`jnl`	Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round. CoRR Manh Hung Nguyen, Lisheng Sun, Nathan Grinsztajn, Isabelle Guyon
2022	B	`conf`	Meta-learning from Learning Curves: Challenge Design and Baseline Results. IJCNN Manh Hung Nguyen, Lisheng Sun-Hosoya, Nathan Grinsztajn, Isabelle Guyon
2022	J	`jnl`	Population-Based Reinforcement Learning for Combinatorial Optimization. CoRR Nathan Grinsztajn, Daniel Furelos-Blanco, Thomas D. Barrett
2021	J	`jnl`	Interferometric Graph Transform for Community Labeling. CoRR Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon
2021	J	`jnl`	Low-Rank Projections of GCNs Laplacian. CoRR Nathan Grinsztajn, Philippe Preux, Edouard Oyallon
2021	—	`conf`	MetaREVEAL: RL-based Meta-learning from Learning Curves. IAL@PKDD/ECML Manh Hung Nguyen, Isabelle Guyon, Lisheng Sun-Hosoya, Nathan Grinsztajn
2021	J	`jnl`	More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences. CoRR Toby Johnstone, Nathan Grinsztajn, Johan Ferret, Philippe Preux
2021	C	`conf`	READYS: A Reinforcement Learning Based Strategy for Heterogeneous Dynamic Scheduling. CLUSTER Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux
2021	A*	`conf`	There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. NeurIPS Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist
2021	J	`jnl`	There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning. CoRR Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist
2020	J	`jnl`	Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling. CoRR Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux
2020	—	`conf`	Geometric deep reinforcement learning for dynamic DAG scheduling. SSCI Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux