Reference Request: Computational Learning Theory Pretty soon I will be finishing up Understanding Machine Learning by Shai Ben-David and Shai Shalev-Shwartz. I absolutely love the subject and want to learn more, the only issue is I'm having trouble finding a book that could come after this. Ultimately, my goal is to read papers in JMLR's COLT.


Is there a book similar to Understanding Machine Learning that would progress my knowledge further and would go well after reading UML?
Is there any other materials (not a book) that could allow me to learn more or prepare me for reading a journal like the one mentioned above?


(also taking courses in this is not really an option so this will be for self-study)

This question was also asked here on AI SE from suggestion of comments.
Another good introductory book is "Foundations of Machine Learning" by Mohri et al.: https://www.amazon.com/Foundations-Machine-Learning-Mehryar-Mohri/dp/0262039400/.  It has a large overlap with the Shai and Shai book, but also quite a bit of content that they don't cover.

There are also good books and surveys on more advanced or specialized topics:


Szepesvári and Lattimore have a nice book on bandits:
https://tor-lattimore.com/downloads/book/book.pdf
Cesa-Bianchi and Lugosi have a difficult but rewarding book on
learning and games: https://www.amazon.com/Prediction-Learning-Games-Nicolo-Cesa-Bianchi/dp/0521841089/
Schapire and Freund have a wonderful book on boosting: https://mitpress.mit.edu/books/boosting


There are more, but I hope this is a good start.