Montag, 18. November 2013

[HIForum] [Kolloquium] Kolloq: Ralf Herbrich (Amazon Research, Berlin)

Hallo alle,

am Montag in einer Woche gibt es wieder einen Kolloquiums-Vortrag.
Sprecher ist Ralf Herbrich, Director of Machine Learning Science at
Amazon. Er baut gerade Amazons neues Forschungslabor in Berlin auf und
war vorher als leitender Wissenschaftler bei Facebook und bei Microsoft
Research.

Der Vortrag ist auch fuer Mitarbeiter und Studierende interessant, die
mal sehen wollen, was man bei Amazon alles so machen kann. Details siehe
unten.

Viele Gruesse, Ulrike Luxburg




This is an invitation to the next "UHH Informatik Kolloquium"
http://www.informatik.uni-hamburg.de/Info/Kolloquium/index.shtml

SPEAKER: Ralf Herbrich, Ph.D.
Director Machine Learning Science, Amazon Berlin
This talk will be held in English
http://www.herbrich.me/

DATE: MONDAY, 25.11.2013 17:15

PLACE: Konrad-Zuse-Hörsaal, Informatikum, B-201
http://www.informatik.uni-hamburg.de/Info/Campus/index.shtml


-------------------------------------
TOPIC: Large-Scale Machine Learning
-------------------------------------


ABSTRACT:
The last ten years have seen a tremendous growth in Internet-based online
services such as search, advertising, gaming and social networking. Today,
it is important to analyze large collections of user interaction data as a
first step in building predictive models for these services as well as
learn these models in real-time. One of the biggest challenges in this
setting is scale: not only does the sheer scale of data necessitate
parallel processing but it also necessitates distributed models; any
user-specific sets of features in a linear or non-linear model yields
models of a size bigger than can be stored in a single system.

In this talk, I will give an introduction to distributed message passing,
a theoretical framework that can deal both with the distributed inference
and storage of models. After an overview of message passing, I will
discuss and present recent advances in approximate message passing which
allows to control the model size as the amount of training data grows. We
will also review how distributed (approximate) message passing can be
mapped to generalized distributed computing and how modeling constraints
map on the system design. In the second part of the talk, I will give an
overview of the application of these techniques to real-world learning
systems, namely:
1. Gamer ranking and matchmaking in TrueSkill and Halo 3
2. AdPredictor click-through rate learning and prediction in sponsored
search
3. User-action models in Facebook's information distribution and
advertising pipeline



BIO:
Ralf Herbrich is Director of Machine Learning at Amazon Berlin, Germany.
In 2011, he worked at Facebook leading the Unified Ranking and Allocation
team. From 2009 to 2011, he was Director of Microsoft's Future Social
Experiences (FUSE) Lab UK working on the development of computational
intelligence technologies on large online data collections. He holds both
a diploma degree in Computer Science in 1997 and a Ph.D. degree in
Statistics in 2000 from TU Berlin. Ralf's research interests include
Bayesian inference and decision making, computer games, kernel methods and
statistical learning theory. Ralf is one of the inventors of the Drivatars
system in the Forza Motorsport series as well as the TrueSkill ranking and
matchmaking system in Xbox 360 Live. He also co-invented the adPredictor
click-prediction technology launched in 2009 in Bing's online advertising
system.




--

------------------------------------------------------------
Prof. Dr. Ulrike von Luxburg
Department of Computer Science, University of Hamburg

Phone: +49 - (0)40 - 42883 2409
http://www.informatik.uni-hamburg.de/ML/contents/people/luxburg/

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