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ORCIDNathanael Jo
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2020 – today
- 2024
[c3]Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. Ho
:
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features. SaTML 2024: 161-193- 2023
- [c2]Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos:
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results. AAAI 2023: 11837-11846 - [c1]Nathanael Jo, Sina Aghaei, Jack Benson, Andrés Gómez, Phebe Vayanos:
Learning Optimal Fair Decision Trees: Trade-offs Between Interpretability, Fairness, and Accuracy. AIES 2023: 181-192 - [i5]Patrick Vossler, Sina Aghaei, Nathan Justin, Nathanael Jo, Andrés Gómez, Phebe Vayanos:
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription. CoRR abs/2307.15691 (2023) - [i4]Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. Ho:
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features. CoRR abs/2310.01679 (2023) - 2022
- [i3]Nathanael Jo, Sina Aghaei, Jack Benson, Andrés Gómez, Phebe Vayanos:
Learning Optimal Fair Classification Trees. CoRR abs/2201.09932 (2022) - [i2]Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos:
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results. CoRR abs/2212.01725 (2022) - 2021
- [i1]Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos:
Learning Optimal Prescriptive Trees from Observational Data. CoRR abs/2108.13628 (2021)
Coauthor Index
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