<p>For a concise overview the ideas you can read <a href="https://maths-people.anu.edu.au/~mendelso/papers/summer02.pdf&amp;ved=2ahUKEwieoPuvqJzjAhUjMuwKHVTCBVcQFjABegQIARAB&amp;usg=AOvVaw2sGYy-mQbnSWWzHIGY2k9U" rel="nofollow noreferrer">Mendelson's notes</a> on the topic. </p>

<p>For a  bit more check out the <a href="https://www.cs.bgu.ac.il/~asml162/Class_Material" rel="nofollow noreferrer">outline</a> (and the links in it) by Kontorovich on how the basic and most fundamental bonds where proved. </p>

<p>For a more extended discussinon you can go over lecture notes of statistical learning courses. Here is one for <a href="https://people.eecs.berkeley.edu/~bartlett/courses/281b-sp06/" rel="nofollow noreferrer">example</a>. </p>

<p>If books are your favorite kind of learning source, any good theoretic statistical learning book will have chapters discussing the basics. Here's links for two <a href="https://rads.stackoverflow.com/amzn/click/com/3540761209" rel="nofollow noreferrer" rel="nofollow noreferrer">relevant</a> <a href="https://cs.nyu.edu/~mohri/mlbook/" rel="nofollow noreferrer">books</a>. </p>

CC BY-SA 4.0
2019-07-04T22:50:01.750Z
2019-07-04T22:50:01.750Z
Meni
<p>I'm looking for references (books, papers, lecture notes etc) on generalization bounds and their proofs. Specifically, I'm looking to fully understand the technique of defining a hypothesis class (or distribution) and using a concentration inequality to obtain a generalization bound.</p>

<p>What are the standard references for such techniques?</p>

CC BY-SA 4.0
2019-04-16T17:09:43.940Z
https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf should be a good starting point.
Sasho Nikolov
<reference-request><machine-learning><lg.learning><upper-bounds>
References on generalization bounds
LYH
# References on generalization bounds

(Asked by: username_0 on 2019-04-16)

I'm looking for references (books, papers, lecture notes etc) on generalization bounds and their proofs. Specifically, I'm looking to fully understand the technique of defining a hypothesis class (or distribution) and using a concentration inequality to obtain a generalization bound.

What are the standard references for such techniques?

**Tags:** reference-request, machine-learning, lg.learning, upper-bounds
> **Comments:**
> **username_1**: https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf should be a good starting point.  

---

## 1 answers

### Answer by username_2 on 2019-07-04. Score: 2 | Accepted answer

For a concise overview the ideas you can read [Mendelson's notes](https://maths-people.anu.edu.au/~mendelso/papers/summer02.pdf&ved=2ahUKEwieoPuvqJzjAhUjMuwKHVTCBVcQFjABegQIARAB&usg=AOvVaw2sGYy-mQbnSWWzHIGY2k9U) on the topic.

For a bit more check out the [outline](https://www.cs.bgu.ac.il/~asml162/Class_Material) (and the links in it) by Kontorovich on how the basic and most fundamental bonds where proved.

For a more extended discussinon you can go over lecture notes of statistical learning courses. Here is one for [example](https://people.eecs.berkeley.edu/~bartlett/courses/281b-sp06/).

If books are your favorite kind of learning source, any good theoretic statistical learning book will have chapters discussing the basics. Here's links for two [relevant](https://rads.stackoverflow.com/amzn/click/com/3540761209) [books](https://cs.nyu.edu/~mohri/mlbook/).


---

