3 papers
cs.LG2024
Can LLMs predict the convergence of Stochastic Gradient Descent?
Oussama Zekri, Abdelhakim Benechehab, Ievgen Redko
Large-language models are notoriously famous for their impressive performance across a wide range of tasks. One surprising example of such impressive performance is a recently iden…
stat.ML2024★ 1 cited
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Romain Ilbert, Malik Tiomoko, Cosme Louart +4
In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensiona…
cs.LG2023
Revisiting invariances and introducing priors in Gromov-Wasserstein distances
Pinar Demetci, Quang Huy Tran, Ievgen Redko +1
Gromov-Wasserstein distance has found many applications in machine learning due to its ability to compare measures across metric spaces and its invariance to isometric transformati…