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Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
In this paper, we present Probabilistic Offline Policy Ranking (POPR), a framework to address OPR problems by leveraging expert data to characterize the probability of a candidate policy behaving like experts, and approximating its entire performance posterior distribution to help with ranking.
A Probabilistic Simulator of Spatial Demand for Product Allocation
Moreover, learning important spatial patterns in offline retail is challenging due to the scarcity of data and the high cost of exploration and experimentation in the physical world.
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