Efficient Data-Driven Optimization with Noisy Data
Published in arXiv (Cornell University) • Feb 8, 2021
Authors:
Bart P. G. Van Parys
Abstract
Classical Kullback-Leibler or entropic distances are known to enjoy certain desirable statistical properties in the context of decision-making with noiseless data. However, in most practical situations the data available to a decision maker is subject to a certain amount of measurement noise. We hen...
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