14. Marco Mondelli - IST: Getting to the bottom of gradient descent methods

# Intro

Marco Mondelli is a group leader at the IST Austrian, focusing on theoretical machine learning and in particular on properties and behaviour of gradient descent methods when used to train overparameterized deep neural networks.

In this interview Marco describes his reasons to start a theoretical machine learning research Group at the IST Austria and several aspects of the IST PhD program.

In the second part of the interview we discuss the research done in his groups and recent publications investigating the reasons behind the efficiency of gradient descent algorithms in optimising deep neural networks.

# References

Marco Mondelli - http://marcomondelli.com/

Mondelli group at IST: https://ist.ac.at/en/research/mondelli-group/

Mean-field particle methods: https://en.wikipedia.org/wiki/Mean-field_particle_methods

Landscape connectivity and dropout stability of SGD solutions for overparameterized neural networks : https://research-explorer.app.ist.ac.at/record/9198

# Interview Timings

03:30 Personal intro & career development

15:47 The Mondelli research group at the IST

32:00 Main research focus

39:00 Recent Publication on the connectivity of loss landscape

56:00 Future research interests