Informatisches Kolloquium WiSe 2013
Ralf Herbrich Ph.D.
Director of Machine Learning Science at Amazon
When: Monday November 25, 2013, 17:15
Large-Scale Machine Learning
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:
- Gamer ranking and matchmaking in TrueSkill and Halo 3
- AdPredictor click-through rate learning and prediction in sponsored search
- User-action models in Facebook's information distribution and advertising pipeline
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 / ML