Hazar Yueksel, Ramon Bertran, et al.
MLSys 2020
Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable to have this pricing policy be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in mixed results in practice. We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.
Hazar Yueksel, Ramon Bertran, et al.
MLSys 2020
Megh Thakkar, Quentin Fournier, et al.
ACL 2024
Natalia Martinez Gil, Dhaval Patel, et al.
UAI 2024
Yuan Cai, Jasmina Burek, et al.
ICML 2021