Learning for Decision and Control in Stochastic Networks

Éditeur :

Springer

Paru le : 2023-06-19

This book introduces the Learning-Augmented Network Optimization (LANO) paradigm, which interconnects network optimization with the emerging AI theory and algorithms and has been receiving a growing attention in network research. The authors present the topic based on a general stochastic network op...
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À propos

Auteur

Éditeur

Collection
n.c

Parution
2023-06-19

Pages
71 pages

EAN papier
9783031315961

Auteur(s) du livre


Longbo Huang, Ph.D. is an Associate Professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University, Beijing, China. He received his Ph.D. in EE from the University of Southern California, and then worked as a postdoctoral researcher in the EECS dept. at University of California at Berkeley before joining IIIS. Dr. Huang previously held visiting positions at the LIDS lab at MIT, the Chinese University of Hong Kong, Bell-labs France, and Microsoft Research Asia (MSRA). He was also a visiting scientist at the Simons Institute for the Theory of Computing at UC Berkeley in Fall 2016. Dr. Huang’s research focuses on decision intelligence (AI for decisions), including deep reinforcement learning, online learning and reinforcement learning, learning-augmented network optimization, distributed optimization and machine learning.

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EAN PDF
9783031315978
Prix
58,01 €
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
1331 Ko
EAN EPUB
9783031315978
Prix
58,01 €
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
4280 Ko

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