"Multi-Armed Bandits: Theory and Applications to Online Learning in Networks"

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Book	Author(s)	Year	Publisher
Multi-Armed Bandits: Theory and Applications to Online Learning in Networks	Qing Zhao		Springer International Publishing
Multi-Armed Bandits: Theory and Applications to Online Learning in Networks	Qing Zhao	2019	Morgan & Claypool
Learning networks: a field guide to teaching and learning online /	Harasim, Linda M.	©1995.	MIT Press,
Online Learning: Theory, Algorithms, and Applications - Ph. D thesis	Shai Shalev-Shwartz
Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications	Igor Aizenberg; Naum N. Aizenberg; Joos P.L. Vandewalle	2000	Springer US
Deep Learning Classifiers with Memristive Networks: Theory and Applications	Alex Pappachen James	1st ed. 2020	Springer International Publishing
Machine Learning Theory to Applications: Theory to Applications	Seyedeh Leili Mirtaheri, Reza Shahbazian	2022	CRC Press
Learning Theory and Online Technologies	Linda Harasim	2017	Routledge
Learning theory and online technology	Linda M Harasim	2012	Routledge
Break the one-armed bandits!	Frank Scoblete	1994	Bonus Books
Learning Together Online: Research on Asynchronous Learning Networks	Starr Roxanne Hiltz; Ricki Goldman	2004	Routledge
Neural networks: theory and applications	Richard J. Mammone	1991	Academic Press
Learning and Generalisation: With Applications to Neural Networks	Mathukumalli Vidyasagar	2nd ed. 2003	Springer London
Innovations in Bayesian Networks: Theory and Applications	Dawn E. Holmes	2008	Springer Berlin Heidelberg
Learning Algorithms Theory and Applications: Theory and Applications	S. Lakshmivarahan	1981	Springer New York
Theory and Practice of Online Learning	Terry Anderson & Fathi Elloumi (Editors)	2004	Athabasca, Alta. : Athabasca University
Socializing the classroom: social networks and online learning	Susan B. Barnes	2012	Lexington Books
Theory and Applications of Multi-Tethers in Space	Panfeng Huang; Fan Zhang	1st ed. 2020	Springer Nature Singapore
A theory of learning and generalization: with applications to neural networks and control systems /	Vidyasagar, M.	[1997], ©1997.	Springer,
Machine Learning Techniques for Online Social Networks	Tansel Özyer; Reda Alhajj	1st ed. 2018	Springer International Publishing
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Multi-Armed Bandits		Machine Learning Engineering	Andriy Burkov	2020
Multi-armed bandits and their economic applications	Brian McCall; John McCall	The Economics of Search	Brian McCall; John McCall	2007	Routledge
Online learning in bandits		Algorithms for reinforcement learning	Csaba Szepesvári	2010	Morgan & Claypool
9 Multi-armed bandits and their economic applications		Exchangeability	McCall J., McCall B.	2004	Routledge
Other applications of multi-armed bandits and CBs		Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices	Enes Bilgin	2020	Packt Publishing
Using value learning with multi-armed bandits		Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques	Micheal Lanham	2020	Packt Publishing Ltd
Multi-armed bandits in experimental design		Hands-on Q-Learning with Python. Practical Q-Learning with OpenAI Gym, Keras and TensorFlow	Nazia Habib	2019	Packt
Chapter 2: Multi-Armed Bandits		Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices	Enes Bilgin	2020	Packt Publishing
Decoupling Exploration and Exploitation in Multi-Armed Bandits		Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow	Nazia Habib	2019	Packt Publishing
Sub-sampling for Multi-armed Bandits	Akram Baransi; Odalric-Ambrym Maillard; Shie Mannor	Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I	Toon Calders; Floriana Esposito; Eyke Hüllermeier; Rosa Meo	2014	Springer Berlin Heidelberg
92 Stateless Algorithms: Multi-Armed Bandits		Charu C. Aggarwal - Neural Networks and Deep Learning. A Textbook-Springer (2018)	Charu C. Aggarwal	0
Application of Multi-Armed Bandits to Sensor Management	Robert B. Washburn	Foundations and Applications of Sensor Management	Alfred Olivier Hero; David Castañón; Doug Cochran; Keith Kastella	2008	Springer US
Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits	Alexandra Carpentier; Alessandro Lazaric; Mohammad Ghavamzadeh; Rémi Munos; Peter Auer	Algorithmic Learning Theory: 22nd International Conference, ALT 2011, Espoo, Finland, October 5-7, 2011, Proceedings	Jyriki Kivinen; Csaba Szepesvári; Esko Ukkonen; Thomas Zeugmann	2011	Springer Berlin Heidelberg
Reinforcement Learning: N-Armed Bandits and Episodes	Miroslav Kubat	An Introduction to Machine Learning	Miroslav Kubat	3rd ed. 2021	Springer International Publishing
Successive Reduction of Arms in Multi-Armed Bandits	Neha Gupta; Ole-Christoffer Granmo; Ashok Agrawala	Research and Development in Intelligent Systems XXVIII: Incorporating Applications and Innovations in Intelligent Systems XIX Proceedings of AI-2011, the Thirty-first SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence	Max Bramer; Miltos Petridis; Lars Nolle	2011	Springer London
Secure Best Arm Identification in Multi-armed Bandits	Radu Ciucanu; Pascal Lafourcade; Marius Lombard-Platet; Marta Soare	Information Security Practice and Experience: 15th International Conference, ISPEC 2019, Kuala Lumpur, Malaysia, November 26–28, 2019, Proceedings	Swee-Huay Heng; Javier Lopez	1st ed. 2019	Springer International Publishing
one-armed bandits		Ka-Ching!	Denise Duhamel	2009	University of Pittsburgh Press
One-armed bandits		Something for nothing	Clyde Brion Davis	1956 ©1955	Lippincott
Multi-armed Bandits, Probability Simulation, and Bayesian Statistics	Rongpeng Li; Aiichiro Nakano	Simulation with Python: Develop Simulation and Modeling in Natural Sciences, Engineering, and Social Sciences	Rongpeng Li; Aiichiro Nakano	1st ed.	Apress
12 A/B Testing and Multi-Armed Bandits		Numsense! Data Science for the Layman (No Math Added)	Annalyn Ng and Kenneth Soo	2017
Chapter	Author(s)	Book	Book Authors	Year	Publisher

Showing 1 to 20 of 100 entries

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