Nathanael Jo

11 papers A* 1C 1Journal 8Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	The Subjectivity of Monoculture. CoRR Nathanael Jo, Nikhil Garg, Manish Raghavan
2025	J	`jnl`	Homogeneous Algorithms Can Reduce Competition in Personalized Pricing. CoRR Nathanael Jo, Kathleen Creel, Ashia Wilson, Manish Raghavan
2025	J	`jnl`	What Does Your Benchmark Really Measure? A Framework for Robust Inference of AI Capabilities. CoRR Nathanael Jo, Ashia Wilson
2024	—	`conf`	Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features. SaTML Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. Ho
2023	J	`jnl`	Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features. CoRR Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. Ho
2023	A*	`conf`	Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results. AAAI Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos
2023	C	`conf`	Learning Optimal Fair Decision Trees: Trade-offs Between Interpretability, Fairness, and Accuracy. AIES Nathanael Jo, Sina Aghaei, Jack Benson, Andrés Gómez, Phebe Vayanos
2023	J	`jnl`	ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription. CoRR Patrick Vossler, Sina Aghaei, Nathan Justin, Nathanael Jo, Andrés Gómez, Phebe Vayanos
2022	J	`jnl`	Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results. CoRR Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos
2022	J	`jnl`	Learning Optimal Fair Classification Trees. CoRR Nathanael Jo, Sina Aghaei, Jack Benson, Andrés Gómez, Phebe Vayanos
2021	J	`jnl`	Learning Optimal Prescriptive Trees from Observational Data. CoRR Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos