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Learning & Adaptive Systems Group

We are part of the Institute for Machine Learning at the Computer Science Department of ETH Zurich. The group is led by Andreas Krause. Our research is in learning and adaptive systems that actively acquire information, reason and make decisions in large, distributed and uncertain domains, such as sensor networks and the Web. The theoretical aspects include statistical machine learning (online, active, large-scale, …), probabilistic reasoning and optimization (submodular, non-convex, …).

Recent Publications

  • Learning Sparse Additive Models with Interactions in High Dimensions
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  • In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS),
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  • Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation
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  • In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS),
  • [bibtex] [abstract]
  • Bounds for Random Constraint Satisfaction Problems via Spatial Coupling
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  • In To appear in ACM-SIAM Symposium on Discrete Algorithms (SODA),
  • [bibtex] [abstract]

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