1 code implementation • 9 Nov 2020 • Nathan Grinsztajn, Olivier Beaumont, Emmanuel Jeannot, Philippe Preux
In this paper, we propose a reinforcement learning approach to solve a realistic scheduling problem, and apply it to an algorithm commonly executed in the high performance computing community, the Cholesky factorization.
no code implementations • 1 Jan 2021 • Nathan Grinsztajn, Philippe Preux, Edouard Oyallon
In this work, we study the behavior of standard GCNs under spectral manipulations.
no code implementations • 4 Jun 2021 • Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon
We present a new approach for learning unsupervised node representations in community graphs.
no code implementations • ICLR Workshop GTRL 2021 • Nathan Grinsztajn, Philippe Preux, Edouard Oyallon
In this work, we study the behavior of standard models for community detection under spectral manipulations.
no code implementations • NeurIPS 2021 • Nathan Grinsztajn, Johan Ferret, Olivier Pietquin, Philippe Preux, Matthieu Geist
We propose to learn to distinguish reversible from irreversible actions for better informed decision-making in Reinforcement Learning (RL).
no code implementations • 29 Sep 2021 • Nathan Grinsztajn, Toby Johnstone, Johan Ferret, Philippe Preux
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
no code implementations • 20 Oct 2021 • Toby Johnstone, Nathan Grinsztajn, Johan Ferret, Philippe Preux
Incorporating prior knowledge in reinforcement learning algorithms is mainly an open question.
no code implementations • 4 Aug 2022 • Manh Hung Nguyen, Lisheng Sun, Nathan Grinsztajn, Isabelle Guyon
With the lessons learned from the first round and the feedback from the participants, we have designed the second round of our challenge with a new protocol and a new meta-dataset.
1 code implementation • NeurIPS 2023 • Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Clément Bonnet, Thomas D. Barrett
Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances.
1 code implementation • 16 Jun 2023 • Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Sasha Abramowitz, Paul Duckworth, Vincent Coyette, Laurence I. Midgley, Elshadai Tegegn, Tristan Kalloniatis, Omayma Mahjoub, Matthew Macfarlane, Andries P. Smit, Nathan Grinsztajn, Raphael 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
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.
1 code implementation • 29 Nov 2023 • Andries Smit, Paul Duckworth, Nathan Grinsztajn, Thomas D. Barrett, Arnu Pretorius
In this context, multi-agent debate (MAD) has emerged as a promising strategy for enhancing the truthfulness of LLMs.