[Turkmath:6834] RKHS Seminars will continue on December 3rd, 2024! Online on Zoom!

[Omur Ugur]

Dear Friends,

We continue with the RKHS Seminars, now with an **expert** in the field, 
on **Tuesday, 3rd of December** at 14:00 (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 (Bridging Machine 
Learning, Dynamical Systems, and Algorithmic Information Theory: 
Insights from Sparse Kernel Flows and PDE Simplification) is on:

- 
https://ougur.iam.metu.edu.tr/rkhs-seminars/2024/10/22/bridging-machine-learning-dynamical-systems-and-algorithmic-information-theory_insights-from-sparse-kernel-flows-and-pde-implification.html

Best wishes,
Omur

### RKHS-Seminars

Title: Bridging Machine Learning, Dynamical Systems, and Algorithmic 
Information Theory: Insights from Sparse Kernel Flows and PDE Simplification

Speaker: Boumediene Hamzi (Dr., Department of Computing and Mathematical 
Sciences, Caltech)

Date / Time: Tuesday, December 3, 2024 / 14:00 (Ankara, Turkey)

Online on Zoom: https://turing-uk.zoom.us/j/5141752978

Abstract: This presentation delves into the intersection of Machine 
Learning, Dynamical Systems, and Algorithmic Information Theory (AIT), 
exploring the connections between these areas. In the first part, we 
focus on Machine Learning and the problem of learning kernels from data 
using Sparse Kernel Flows. We draw parallels between Minimum Description 
Length (MDL) and Regularization in Machine Learning (RML), showcasing 
that the method of Sparse Kernel Flows offers a natural approach to 
kernel learning. By considering code lengths and complexities rooted in 
AIT, we demonstrate that data-adaptive kernel learning can be achieved 
through the MDL principle, bypassing the need for cross-validation as a 
statistical method.

Transitioning to the second part of the presentation, we shift our 
attention to the task of simplifying Partial Differential Equations 
(PDEs) using kernel methods. Here, we utilize kernel methods to learn 
the Cole-Hopf transformation, transforming the Burgers equation into the 
heat equation. We argue that PDE simplification can also be seen as an 
MDL and a compression problem, aiming to make complex PDEs more 
tractable for analysis and solution. While these two segments may 
initially seem distinct, they collectively exemplify the multifaceted 
nature of research at the intersection of Machine Learning, Dynamical 
Systems, and AIT, offering preliminary insights into the synergies that 
arise when these fields converge.


#### Biography
Boumediene Hamzi is currently a Senior Scientist at the Department of 
Computing and Mathematical Sciences, Caltech. He is also co-leading the 
Research Interest Group on Machine Learning and Dynamical Systems at the 
Alan Turing Institute. Broadly speaking, his research is at the 
interface of Machine Learning and Dynamical Systems.


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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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