Decentralized online learning: decision making under uncertainty and with crowdsourced data
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In this talk I will discuss a type of learning, referred to as regret learning, commonly used in dealing with decision making under uncertainty in the environment, which may also include other similar users. A common feature shared by this type of learning algorithms is to use a combination of "exploration" , where the user samples different options to find out good actions to take, and "exploitation" , where the user takes what he/she believes to be good actions to maximize his/her gain. The design of a good algorithm boils down to determining when to explore, when to exploit and how. This is made more complex when there are multiple uncoordinated such users present in the system. I will go through a few example algorithms and use a number of applications to motivate and illustrate this learning process, including resource allocation in wireless networks and building better classifiers using crowdsourced data.