Best resource for concentration of measure? What are the best resources to learn concentration of measure phenomenon and concentration inequalities? I have heard that Talagrand's papers are good, but they are not particularly good read.
Any help is appreciated. Thanks a lot!
 A: In addition to the material mentioned by air,


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*Concentration of Measure Inequalities in Information Theory, Communications and Coding 

*Lecture notes by Lugosi  (Essentially superceeded by Concentration Inequalities: A Nonasymptotic Theory of Independence by Boucheron, Lugosi and Massart, but freely available online)

*Concentration of Measure for the Analysis of Randomized Algorithms by Dubhashi ,Panconesi

*Terry Tao's blog

*Lecture notes on machine learning (e.g. this set by Wasserman)
A: Two very accessible resources on concentration inequalities are the following:

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*Ramon van Handel's lecture notes on "Probability in high dimension"


*The book Concentration Inequalities: A Nonasymptotic Theory of Independence by Boucheron, Lugosi and Massart.
A: In Marseille, France, Sudeep Kamath delivered an excellent four-part video lecture on concentration of measure, and writes mathematical proofs for entropic and transport-related concentration inequalities:
https://www.youtube.com/results?search_query=Sudeep+Kamath+%3A+Concentration+of+Measure

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*Variance bounds

*Information inequalities

*Entropy method

*Transportation method

Based on this outline alone, his lectures seem to resemble the table of contents of:

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*Concentration Inequalities: A Nonasymptotic Theory of Independence by  Stéphane Boucheron, Gábor Lugosi, and Pascal Massart, 2013.

For those interested, the transportation-information inequalities that appear in concentration of measure can be discussed here.
A: I like Roman Vershynin's book High Dimensional Probability
