6533b7d0fe1ef96bd125b484
RESEARCH PRODUCT
From optimization to algorithmic differentiation: a graph detour
Samuel Vaitersubject
Signaux sur graphesOptimisation convexe[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]High dimensional dataGraph signalsStatistiques en grande dimensionAutomatic differentiation[MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC][MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC][STAT.ML] Statistics [stat]/Machine Learning [stat.ML]Convex optimizationDifférentiation automatiquedescription
This manuscript highlights the work of the author since he was nominated as "Chargé de Recherche" (research scientist) at Centre national de la recherche scientifique (CNRS) in 2015. In particular, the author shows a thematic and chronological evolution of his research interests:- The first part, following his post-doctoral work, is concerned with the development of new algorithms for non-smooth optimization.- The second part is the heart of his research in 2020. It is focused on the analysis of machine learning methods for graph (signal) processing.- Finally, the third and last part, oriented towards the future, is concerned with (automatic or not) differentiation of algorithms for learning and signal processing.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-14 |