[Turkmath:6957] RKHS Seminars will continue on January 14th, 2025! Online on Microsoft Teams!

[Omur Ugur]

Dear Friends,

We continue with the RKHS Seminars, now with an **expert** in the field, 
on **Tuesday, 14th of January** 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 (Causal Effect Estimation 
with Kernels) is on:

- 
https://ougur.iam.metu.edu.tr/rkhs-seminars/2024/11/23/causal-effect-estimation-with-kernels.html

Best wishes,
Omur

### RKHS-Seminars

Title: Causal Effect Estimation with Kernels

Speaker: Arthur Gretton (Gatsby Computational Neuroscience Unit, Centre 
for Computational Statistics and Machine Learning, University College 
London (UCL), UK)

Date / Time: Tuesday, January 14, 2025 / 15:30 (Ankara, Turkey)

Online on Microsoft Teams: 
https://events.teams.microsoft.com/event/79e3acc6-aea4-4e98-9f78-99826b0e5720@b0a2e24d-d188-4a4c-a1e4-82162e060566
(you need to register first in order to get the actual link to join the 
meeting)

Abstract: A fundamental causal modelling task is to predict the effect 
of an intervention (or treatment) on an outcome, given 
context/covariates. Examples include predicting the effect of a medical 
treatment on patient health given patient symptoms and demographic 
information, or predicting the effect of ticket pricing on airline sales 
given seasonal fluctuations in demand. The problem becomes especially 
challenging when the treatment and context are complex (for instance, 
“treatment” might be a web ad design or a radiotherapy plan), and when 
only observational data is available (i.e., we have access to historical 
data, but cannot intervene ourselves). I will give an overview of some 
practical kernel methods for estimating causal effects of complex, high 
dimensional treatments from observational data. The approach is based on 
kernel conditional feature means, which represent conditional 
expectations of relevant model features. The methods will be applied to 
modelling employment outcomes for the US Job Corps program for 
Disadvantaged Youth.

#### Biography
Arthur Gretton is a Professor with the Gatsby Computational Neuroscience 
Unit; director of the Centre for Computational Statistics and Machine 
Learning (CSML) at UCL; and Research Scientist at Google Deepmind. He 
received degrees in Physics and Systems Engineering from the Australian 
National University, and a PhD with Microsoft Research and the Signal 
Processing and Communications Laboratory at the University of Cambridge. 
He previously worked at the MPI for Biological Cybernetics, and at the 
Machine Learning Department, Carnegie Mellon University.

Arthur’s recent research interests in machine learning include causal 
inference and representation learning, design and training of generative 
models (implicit: Wasserstein gradient flows, GANs; and explicit: 
energy-based models), and nonparametric hypothesis testing.

He has been an associate editor at IEEE Transactions on Pattern Analysis 
and Machine Intelligence from 2009 to 2013, an Action Editor for JMLR 
since April 2013, an Area Chair for NeurIPS in 2008 and 2009, a Senior 
Area Chair for NeurIPS in 2018 and 2021, an Area Chair for ICML in 2011 
and 2012, a Senior Area Chair for ICML in 2022, a member of the COLT 
Program Committee in 2013, and a member of Royal Statistical Society 
Research Section Committee since January 2020. Arthur was program chair 
for AISTATS in 2016 (with Christian Robert), tutorials chair for ICML 
2018 (with Ruslan Salakhutdinov), workshops chair for ICML 2019 (with 
Honglak Lee), program chair for the Dali workshop in 2019 (with Krikamol 
Muandet and Shakir Mohammed), and co-organiser of the Machine Learning 
Summer School 2019 in London (with Marc Deisenroth).

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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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