Sh. Song and J. Wellner, “How many distribution functions are there? Bracketing entropy bounds for high-dimensional distribution functions,” 2008.
Shannon Sampling and Learning Theory
These two papers by Steve Smale and Ding-Xuan Zhou:
Shannon Sampling and Function Reconstruction from Point Values
Shannon Sampling II. Connections to Learning Theory
Probability Inequalities and Machine Learning
Michael Steele has this course on “Probability Inequalities and Machine Learning”. This is the kind of course I’ve really liked to take. Anyway, for most of us who cannot attend the course, following his suggested reading may help somewhat.
There are some known papers/lecture notes in the reading list; such as Gabor Lugosi’s “Concentration of measure inequality” which is very readable.
There are also some other books that I hadn’t heard of it (what a shame), but seems to be nice: Pascal Massart‘s Concentration Inequalities and Model Selection.
Also in the page I found this paper by Mease and Wyner entitled “Evidence Contrary to the Statistical View of Boosting”. I definitely should read it.
well … that’s it for now!