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Charles-Etienne Joseph

Alumni

Publications

$\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers

Benjamin Therien

Charles-Etienne Joseph

Edouard Oyallon

Eugene Belilovsky

Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can strug… (see more)gle to optimize unseen tasks (*meta-generalize*), especially when training networks wider than those seen during meta-training. To address this, we derive the Maximal Update Parametrization (

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Continual Pre-training of MoEs: How robust is your router?

Benjamin Therien

Charles-Etienne Joseph

Zain Sarwar

Ashwinee Panda

Anirban Das

Shi-Xiong Zhang

Stephen Rawls

Sambit Sahu

Eugene Belilovsky

2025-09-25

TMLR (accepted)

Can We Learn Communication-Efficient Optimizers?

Charles-Etienne Joseph

Benjamin Therien

Abhinav Moudgil

Eugene Belilovsky

2024-12-31

Trans. Mach. Learn. Res. (published)

μLO: Compute-Efficient Meta-Generalization of Learned Optimizers

Benjamin Therien

Charles-Etienne Joseph

Edouard Oyallon

Eugene Belilovsky

2024-10-09

NeurIPS.cc/2024/Workshop/OPT (published)

Learning Optimizers for Local SGD

Charles-Etienne Joseph

Benjamin Therien

Abhinav Moudgil

Eugene Belilovsky

2023-10-26

NeurIPS.cc/2023/Workshop/Federated_Learning (poster)