Researcher in Optimization
and Machine Learning

• I will be joining Tübingen in February 2022 to work on the TRINOM-DS project led by Peter Ochs and Jalal Fadili.

I am a researcher in applied mathematics and machine learning. I recently obtained my PhD degree working at the IRIT laboratory of Toulouse under the supervision of Cédric Févotte, Édouard Pauwels and Jérôme Bolte. I was a member of the FACTORY project.

I graduated from INSA Toulouse and Université Paul Sabatier where I obtained master degrees respectively in "Applied Mathematics and Modeling" and "Applied Mathematics: Partial Differential Equations". I did my master thesis under the supervision of Pierre Weiss and Frédéric de Gournay.

My research focuses on optimization algorithms for deep learning, and machine learning in general. I used to give tutorials and practicals at INP-ENSEEIHT where I taught probabilities, statistics and integration theory to master students.

Research interests

• Optimization: non-convex, non-smooth, stochastic, etc.
• Deep Learning
• Theory of Machine Learning and Statistics

Recent work

Inertial Newton algorithms avoiding strict saddle points

C. Castera (2021)
Preprint

arXiv

Second-order step-size tuning of SGD for non-convex optimization

C. Castera, J. Bolte, C. Févotte, E. Pauwels (2021)
To appear in Neural Processing Letters

arXiv

An inertial Newton algorithm for deep learning

C. Castera, J. Bolte, C. Févotte, E. Pauwels (2021)
Journal of Machine Learning Research

JMLR | Hal | Code

Python packages for the INNA algorithm

Pytorch Version | Keras Version | Tensorflow Version

Poster: the INNA algorithm

Poster presented at NeurIPS 2019

List of publications

• Full list

Contact