Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

3 July 2020

Papers citing "Model Distillation for Revenue Optimization: Interpretable Personalized Pricing"

10 / 10 papers shown

Title
Learning Prescriptive ReLU Networks Wei-Ju Sun Asterios Tsiourvas 21 2 0 01 Jun 2023
Tight Mixed-Integer Optimization Formulations for Prescriptive Trees Max Biggs Georgia Perakis 16 1 0 28 Feb 2023
Participatory Personalization in Classification Hailey J James Chirag Nagpal Katherine A. Heller Berk Ustun 39 4 0 08 Feb 2023
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs Benjamin Fuhrer Yuval Shpigelman Chen Tessler Shie Mannor Gal Chechik E. Zahavi Gal Dalal 33 4 0 05 Jul 2022
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction Vinith Suriyakumar Marzyeh Ghassemi Berk Ustun 41 6 0 04 Jun 2022
Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data Max Biggs OffRL 18 1 0 16 Feb 2022
Regulatory Instruments for Fair Personalized Pricing Renzhe Xu Xingxuan Zhang Pengbi Cui Yangqiu Song Zheyan Shen Jiazheng Xu 11 15 0 09 Feb 2022
Loss Functions for Discrete Contextual Pricing with Observational Data Max Biggs Ruijiang Gao Wei-Ju Sun 33 10 0 18 Nov 2021
Interpretable Personalized Experimentation Han Wu S. Tan Weiwei Li Mia Garrard Adam Obeng Drew Dimmery Shaun Singh Hanson Wang Daniel R. Jiang E. Bakshy 33 5 0 05 Nov 2021
Dynamic Pricing in High-dimensions Adel Javanmard Hamid Nazerzadeh 69 138 0 24 Sep 2016