The classical Kullback-Leibler distance is known to enjoy desirable statistical properties in the context of decision-making with noiseless data. However, in most practical situations data is subject to a certain amount of measurement noise. We hence study here data-driven prescription problems in which the data is corrupted by a known noise source. We derive efficient data-driven formulations in this noisy regime and indicate that they enjoy an entropic optimal transport interpretation.

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doi.org/10.1016/j.orl.2024.107089
Operations Research Letters
Stochastics

van Parys, B. (2024). Efficient data-driven optimization with noisy data. Operations Research Letters, 54, 107089:1–107089:8. doi:10.1016/j.orl.2024.107089