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Biggs, M., Perakis, G.
Tightness of Prescriptive Tree-Based Mixed-Integer Optimization Formulations.
Submitted, 2023.
- Supersedes Dynamic Routing with Tree Based Value Function Approximations, October 2020
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Biggs, M.,
Convex Loss Functions for Contextual Pricing with Transaction Data.
Major Revision, Management Science, 2022.
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Biggs, M., Gao, R., Sun, W.
Loss Functions for Discrete Contextual Pricing with Observational Data.
Major Revision, Operations Research, 2021.
- Selected as a spotlight presentation RMP 2022
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Gao, R., Biggs, M., Sun, W., Han, L.
Enhancing Counterfactual Classification Performance via Self-Training.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), 2022.
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Biggs, M., Sun, W., Ettl, M.
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing.
Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.
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Alley, M., Biggs, M., Hariss, R., Herrmann, C., Li, M., Perakis,G.
Pricing for Heterogeneous Products: Analytics for Ticket Reselling.
Manufacturing & Service Operations Management. 2022
- Finalist MSOM practice based research competition 2019
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Biggs, M., Hariss, R., Perakis, G.
Constrained Optimization of Objective Functions Determined from Random Forests.
Production and Operations Management, 2022.
- Winner of Data Mining Theoretical Best Paper Competition INFORMS 2018
- Previously Optimizating Objective Functions Determined from Random Forests, June 2017
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Biggs, M., Perakis, G.
A Ranking Algorithm for Tramp Shipping in the Spot Market.
R & R Management Science,
- Service Science Best Cluster Award Finalist INFORMS 2017