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!
2026-03-27 09:48:16.1774604896
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Best resource for concentration of measure?
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In addition to the material mentioned by air,
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)
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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
- Variance bounds
- Information inequalities
- Entropy method
- Transportation method
Based on this outline alone, his lectures seem to resemble the table of contents of:
- 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.
Two very accessible resources on concentration inequalities are the following:
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.