| Constrained optimization of objective functions determined from random forests M Biggs, R Hariss, G Perakis Production and Operations Management 32 (2), 397-415, 2023 | 58* | 2023 |
| Model distillation for revenue optimization: Interpretable personalized pricing M Biggs, W Sun, M Ettl International Conference on Machine Learning, 946-956, 2021 | 38 | 2021 |
| Pricing for heterogeneous products: Analytics for ticket reselling M Alley, M Biggs, R Hariss, C Herrmann, ML Li, G Perakis Manufacturing & Service Operations Management 25 (2), 409-426, 2023 | 21 | 2023 |
| Enhancing Counterfactual Classification Performance via Self-Training R Gao, M Biggs, W Sun, L Han Proceedings of the AAAI conference on artificial intelligence 36 (6), 6665-6673, 2022 | 10* | 2022 |
| Loss functions for discrete contextual pricing with observational data M Biggs, R Gao, W Sun arXiv preprint arXiv:2111.09933, 2021 | 10 | 2021 |
| Dynamic routing with tree based value function approximations M Biggs, G Perakis Available at SSRN 3680162, 2020 | 6 | 2020 |
| Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data M Biggs arXiv preprint arXiv:2202.10944, 2022 | 5* | 2022 |
| Imputing counterfactual data to faciltiate machine learning model training R Gao, W Sun, M Biggs, Y Drissi, M Ettl US Patent App. 17/654,617, 2023 | | 2023 |
| Tightness of prescriptive tree-based mixed-integer optimization formulations M Biggs, G Perakis arXiv preprint arXiv:2302.14744, 2023 | | 2023 |
| Counterfactual self-training R Gao, W Sun, M Biggs, M Ettl, Y Drissi US Patent App. 17/402,367, 2023 | | 2023 |
| Context-Based Pricing for Revenue Optimization with Applications to the Airline Industry S Subramanian, M Ettl, M Biggs, W Sun, Y Drissi Annual Hawaii International Conference on System Sciences, 2023 | | 2023 |
| Context-based Pricing for Revenue Optimization with Applications to the Airline Industry M Ettl, S Subramanian, Y Drissi, W Sun, M Biggs | | 2023 |
| Domain-specific constraints for predictive modeling P Harsha, BL Quanz, S Subramanian, W Sun, M Biggs US Patent App. 17/135,913, 2022 | | 2022 |
| Loss augmentation for predictive modeling P Harsha, BL Quanz, S Subramanian, W Sun, M Biggs US Patent App. 17/135,920, 2022 | | 2022 |
| Split-net configuration for predictive modeling P Harsha, BL Quanz, S Subramanian, W Sun, M Biggs US Patent App. 17/135,925, 2022 | | 2022 |
| Integrated segmentation and interpretable prescriptive policies generation M Biggs, W Sun, S Subramanian, M Ettl US Patent App. 17/111,212, 2022 | | 2022 |
| Ticket Pricing via Prescriptive Model Distillation W Sun, S Subramanian, M Biggs, Y Drissi, M Ettl INFORMS Annual Meeting, 2021 | | 2021 |
| Prescriptive analytics in operations problems: a tree ensemble approach MMR Biggs Massachusetts Institute of Technology, 2019 | | 2019 |
Georgia PerakisWilliam F. Pounds Professor of Operations Management and Operations ResearchVerified email at mit.edu
