Tutorial on Distributed Strategic Learning For Engineers
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Materials:
 detailed slides
 BOOK: H. Tembine, Distributed Strategic Learning for Wireless Engineers, CRC Press/ Taylor & Francis, 496 pages, May 2012, ISBN: 9781439876442. Download here
Audience: Engineers, Masters, Ph.D. Students, Researchers
This tutorial course will revisit the fundamental tools of distributed strategic learning in view of their applications to wireless networks. The course will be subdivided into a theoretical part where the classical methods and results for distributed learning are introduced, and an application part where practical considerations in engineering are visited. Each part will decline successively the basic tools and applications, the advanced methods and results known today, as well as current research activities.
A precise outline is given below:
Part I: THEORETICAL NOTIONS
 Markov trees
 Dynamical systems
 Stochastic approximation
 Differential inclusion
 Basics of matrix games

Basics of random matrix games (RMGs)
 Basics of robust games
 Basics of dynamic robust games
 Dynamic solution concepts/stationary solution concepts
 Research today:
 fast convergence of iterative learning patterns,
 hitting time to a set,
 frequency of visits
 Introduction to strategy learning
(STRATEGY LEARNING and PERFORMANCE ESTIMATIONS)
 Strategylearning schemes [for equilibrium, Pareto point, stackelberg solution, satisfactory point, coalition selection]
 Payofflearning,
 Combined learning (CODIPAS)
 Heterogeneous learning
 Hybrid learning
 Learning with random updates
 Risksensitive strategic learning in dynamic robust games
 Combined learning for continuous action space
 Mean field learning
 Learning in random matrix games (RMGs). CODIPAS for meanvariance learning
 Research today:
 Best learning algorithms for stability/performance tradeoff,
 How to learn global optima in a fully distributed way?
Part II: APPLICATIONS TO WIRELESS NETWORKS, COMMUNICATIONS AND NETWORK ECONOMICS
(WIRELESS NETWORKS and COMMUNICATIONS)
 Distributed learning for parallel routing and frequency selection
 Strategic Learning in usercentric network selection
 Combined learning under noise in WLAN
 Cost of learning and Quality of Experience (QoE) in LTE
 Distributed Learning for Network Security
 Learning under uncertainty for Network MIMO
 Coalitional learning for cognitive radios
 Research today:
 Why should we care on distributed strategic learning?
 How to extract useful information from outdated and noisy measurements?
 How to extract useful information from big data?
(ECONOMICS OF NETWORKS)
 Mean field learning for the smart grid
 Hierarchical learning for network design
 Risksensitive learning for the economics of cloud computing
 Research today:
 Simultaneous, coalitional and mutual learning
 Learn how to flatten the peaks in largescale networks
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