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Foundations of Machine Learning
Awards
Kay H. Brodersen received a Trainee Abstract Award at HBM 2012 for his work on 'Model-Based Clustering Using Generative Embedding.' Kay will be giving a talk on his results in Beijing on 12 June.
François Cellier received the McLeod Founder's Award of the Society for Modeling and Simulation International.
Spotlight
Computational models of brain connectivity, coupled with machine learning algorithms, make it possible to infer neuronal disease mechanisms from
non-invasive functional magnetic resonance imaging (fMRI) data in humans.
This illustration shows how dynamic systems models can be used for reducing
complex (high-dimensional) brain activity data to a simple (low-dimensional)
and mechanistically interpretable representation (Brodersen et al., PLoS
Comput. Biol. 2011). Please also see the summary on ETH Life.
Introduction
| We study the statistical and algorithmic principles behind learning from data. One key issue is the notion of complexity, and how to control it while finding a model that is in agreement with the observed measurements. We focus our efforts on structured data, investigating approaches such as clustering, graphical models and dynamical systems. A major effort of our algorithmic and modeling work is devoted to quantify the robustness of the learned structures, i.e., to provide the data analyst with uncertainty estimates of models and methods. |
Projects
Inference on discrete structures
| Many problems demand that discrete structures like rankings, trees, or graphs be classified or clustered based on similarity. The aim of this work is to research inference on discrete data structures, with the goal of obtaining an hierarchical model family capturing the discrete nature of the data and to derive robust optimisation techniques. The expected contributions include both a theoretical understanding of inference on discrete data structures and a viable model toolkit. From a theoretical point of view, it contributes to an understanding of the relation between model complexity and algorithmic complexity. How does inference on discrete data/combinatorial objects behave in terms of robustness, consistency, and stability? Notions of complexity. From an application’s perspective, the enabling technology to analyse discrete data is fruitful in areas such diverse as Economics (e.g. rankings in preference modelling), the Social Sciences (e.g. consensus finding) as well as the Natural Sciences (e.g. large scale graph data in proteomics). |
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Contact: Ludwig Busse |
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Collaborators: SWISS Air Lines, Clariden Leu Investment Products |
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