Ludovic Stephan

According to our database, Ludovic Stephan authored at least 15 papers between 2019 and 2024.

Collaborative distances:

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

2024

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs.

Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

CoRR, 2024

Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions.

Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Luca Pesce, Ludovic Stephan

CoRR, 2024

Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs.

Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

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

A non-backtracking method for long matrix and tensor completion.

Ludovic Stephan, Yizhe Zhu

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Escaping mediocrity: how two-layer networks learn hard single-index models with SGD.

Luca Arnaboldi, Florent Krzakala, Bruno Loureiro, Ludovic Stephan

CoRR, 2023

Learning Two-Layer Neural Networks, One (Giant) Step at a Time.

Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

CoRR, 2023

Universality laws for Gaussian mixtures in generalized linear models.

Yatin Dandi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro, Lenka Zdeborová

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

Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation.

Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan

Proceedings of the International Conference on Machine Learning, 2023

From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks.

Luca Arnaboldi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Gaussian Universality of Linear Classifiers with Random Labels in High-Dimension.

Federica Gerace, Florent Krzakala, Bruno Loureiro, Ludovic Stephan, Lenka Zdeborová

CoRR, 2022

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks.

Rodrigo Veiga, Ludovic Stephan, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sparse random hypergraphs: Non-backtracking spectra and community detection.

Ludovic Stephan, Yizhe Zhu

Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

2021

A simpler spectral approach for clustering in directed networks.

Simon Coste, Ludovic Stephan

CoRR, 2021

2019

Robustness of Spectral Methods for Community Detection.

Ludovic Stephan, Laurent Massoulié

Proceedings of the Conference on Learning Theory, 2019

Planting trees in graphs, and finding them back.

Laurent Massoulié, Ludovic Stephan, Don Towsley

Proceedings of the Conference on Learning Theory, 2019