[Turkmath:7006] RKHS Seminars will continue on February 13th, 2025!

[Omur Ugur]

Dear Friends,

We continue with the RKHS Seminars, now with an **expert** in the field, 
on **Thursday, February 13, 2025** at 15:30 (Ankara, Turkey)!

The RKHS-Seminar Website is on https://iam.metu.edu.tr/rkhs-seminars. 
Please, do not miss the scheduled talk below!

Again, for a quick reminder, the event's page (Functional Bilevel 
Optimization: Theory and Algorithms) is on:

- 
https://ougur.iam.metu.edu.tr/rkhs-seminars/2024/11/24/functional-bilevel-optimization-theory-and-algorithms

Best wishes,
Omur

### RKHS-Seminars

Title: 
https://ougur.iam.metu.edu.tr/rkhs-seminars/2024/11/24/functional-bilevel-optimization-theory-and-algorithms

Speaker: Michael N. Arbel (THOTH Team, INRIA Grenoble - Rhône-Alpes, France)

Date / Time: Thursday, February 13, 2025 / 15:30 (Ankara, Turkey)

Online (MS Teams): 
https://events.teams.microsoft.com/event/c4efb03a-cad0-49d3-a5b6-fd12a6e5b02b@b0a2e24d-d188-4a4c-a1e4-82162e060566
(please self-register first to get the link for the meeting)

Abstract: Bilevel optimization is widely used in machine learning, where 
an outer objective depends on the minimization of an inner problem. Most 
studies assume strong convexity of the inner objective with respect to 
finite-dimensional parameters, a restrictive setting for modern ML. We 
introduce a functional perspective on bilevel optimization, enabling 
richer models like neural networks and kernel methods while ensuring 
theoretical rigor and practical efficiency. We propose scalable 
algorithms for functional bilevel problems and demonstrate their 
benefits in instrumental regression and reinforcement learning. 
Theoretically, we establish novel generalization error bounds when the 
functional space is a Reproducing Kernel Hilbert Spaces, using empirical 
process theory and maximal inequalities for U-process, providing 
insights into the statistical accuracy of gradient-based methods for 
bilevel optimization.

#### Biography
Michael N. Arbel is a Research Scientist (Chargé de recherche) at INRIA 
Grenoble - Rhône-Alpes, THOTH team. Before that, he was a Starting 
Research Fellow at the same team working with Julien Mairal. He 
completed his PhD in 2021 at the Gatsby Computational Neuroscience Unit 
of University College London under the supervision of Arthur Gretton. 
Even before that, he graduated from Ecole polytechnique with a focus in 
Applied Mathematics and obtained a Masters Degree in Mathematics, 
Machine Learning and Computer Vision (MVA) from ENS Paris-Saclay. He 
also worked as a Computer Vision Engineer at Prophesee where he 
developed tracking algorithms based on signals from neuromorophic cameras.

His research interests include Unsupervised Representation Learning, 
Non-convex optimization for Machine Learning and High Dimensional 
Sampling. He finds problems arising from natural sciences and physics to 
be a great source of inspiration and a good way to find a balance 
between theory and practice.

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Dr. Omur Ugur                  | Middle East Technical University
http://users.metu.edu.tr/ougur | Institute of Applied Mathematics
Tel.: +90(312) 210 29 87       |             06800 Ankara, Turkey
Fax : +90(312) 210 29 85       |
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