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Probabilistic Decision-Making Under Model Uncertainty

Probabilistic Decision-Making Under Model Uncertainty

This video was recorded at Carnegie Mellon Machine Learning Lunch seminar. Partially Observable Markov Decision Processes offer a rich mathematical framework for decision-making under uncertainty. In recent years, a number of methods have been developed to optimize the choice of action, given a parametric model of the domain. In many applications, however, this model must be learned using a finite set of trajectories. When this data proves difficult or expensive to collect, it is often the case that the resulting model is poorly or imprecisely defined. In this talk, I will present two recent results on the topic of decision-making under model uncertainty. In the first half, I will describe a method for estimating the bias and variance of the value function in terms of the statistics of the... Show More


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