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Springer's Chapter on Learned data structures

by Paolo Ferragina on 2020/04/07

Very recently, the unexpected combination of data structures and machine learning has led to the development of a new area of research, called learned data structures. Their distinguishing trait is the ability to reveal and exploit patterns and trends in the input data for achieving more efficiency in time and space, compared to previously known data structures.

We provide the first comprehensive survey of these results in a chapter of the new book “Recent Trends in Learning From Data” published by Springer. The chapter is available on the publisher’s website, and a pre-print is available here (PDF).

Cite as: Ferragina P., Vinciguerra G. (2020) Learned Data Structures. In: Oneto L., Navarin N., Sperduti A., Anguita D. (eds) Recent Trends in Learning From Data. Studies in Computational Intelligence, vol 896. Springer.

	Title = {Learned Data Structures},
	Author = {Ferragina, Paolo and Vinciguerra, Giorgio},
	Booktitle = {Recent Trends in Learning From Data},
	Editor = {Oneto, Luca and Navarin, Nicol{\`o} and Sperduti, Alessandro and Anguita, Davide},
	Isbn = {978-3-030-43883-8},
	Pages = {5--41},
	Publisher = {Springer International Publishing},
	Doi = {10.1007/978-3-030-43883-8_2},
	Url = {},
	Year = {2020}}