[talks] Donghun Lee will present his Pre-FPO, "On Finite Horizon Behaviors of Machine Learning: the Max-Operator Bias in Q-learning and the Framework of Learning to Learn Optimally" on Thursday, November 29, 2018 at 1:30pm in CS 105

[Nicki Gotsis]

Donghun Lee will present his Pre-FPO on Thursday, November 29, 2018 at 1:30pm in CS 105. 

The members of his committee are Warren Powell (ORFE) (adviser), Ryan Adams, and Peter Ramadge (EE). Everyone is invited to attend his talk. The talk title and abstract follow below. 

Title: 
On Finite Horizon Behaviors of Machine Learning: the Max-Operator Bias in Q-learning and the Framework of Learning to Learn Optimally 

Abstract: 
With greater popularity of machine learning in industry and growing number of available packages and toolkits, many end users of machine learning face the problem of deploying machine learning algorithms to their applications. Most algorithms with asymptotic guarantees leave finite-time issues such as initialization or tuning at the mercy of the end users, to whom the burden may cause undesirable outcomes in practice with finite time horizon. This talk will cover my research on finite time horizon behavior of machine learning algorithms: firstly on Q-learning algorithm, and secondly on the problem of "learning to learn optimally". First, I will review the positive bias innate in Q-learning algorithm, and present a method to correct the bias on-the-fly. Then, I will address the problem of applying machine learning algorithm to finite time horizon problems to formulate the "learning to learn optimally" framework. Using the framework, I will construct a knowledge-gradient based algorithm for adaptive deployment of machine learning application with finite time horizon. Some work in progress will be summarized at the end of the talk. 

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