New approaches to statistical learning theory

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Optimal Rates for Random Fourier Features (2015, methodology): “-sampledata and alsoin online learning applications. The RFF technique has found wide applicability in several areas such as fast function-to-function regression [12], differential privacy preserving [2] and causal discovery [10]. Despite the success of the RFF method,surprisingly, verylittle is kno”
Robust Optimization using Machine Learning for Uncertainty Sets (2014, background): “These bounds do what learning theory is designed to do [Bousquet, 2003], which is provide insight into the important quantities for learning and how they scale.”
On combining machine learning with decision making (2011, methodology): “ar bound for (23) might be derived using the same method. Generalization bounds are probabilistic guarantees that are useful for showing what variables may be important in the generalization process (Bousquet, 2003). The vast majority of works on generalization analysis are mainly interested in prob”