Algorithmic Aspects of Machine Learning
Principles of Very Large Scale Modeling
Empirical Portfolio Selection
What cannot be learned with Bethe Approximations
From Proteins to Robots: Learning to Optimize with Confidence
Non-standard Geometries and Data Analysis
Modeling reality without sacrificing data: Inferentially tractable models for complex social systems
On the Computational and Statistical Interface and "BIG DATA"
Learning Deformable Models