Search Results for author: Nathan Grinsztajn

Found 11 papers, 4 papers with code

Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling

1 code implementation9 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.

Combinatorial Optimization reinforcement-learning +2

A spectral perspective on GCNs

no code implementations1 Jan 2021 Nathan Grinsztajn, Philippe Preux, Edouard Oyallon

In this work, we study the behavior of standard GCNs under spectral manipulations.

Interferometric Graph Transform for Community Labeling

no code implementations4 Jun 2021 Nathan Grinsztajn, Louis Leconte, Philippe Preux, Edouard Oyallon

We present a new approach for learning unsupervised node representations in community graphs.

Low-Rank Projections of GCNs Laplacian

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.

Community Detection

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

no code implementations4 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.

AutoML Meta-Learning

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

1 code implementation29 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.

Benchmarking

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