Nathanael Jo

According to our database, Nathanael Jo authored at least 11 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Alignment has a Fantasia Problem.

Nathanael Jo, Zoe De Simone, Mitchell Gordon, Ashia Wilson

CoRR, April, 2026

Incentives shape how humans co-create with generative AI.

Nathanael Jo, Manish Raghavan

CoRR, April, 2026

The Subjectivity of Monoculture.

Nathanael Jo, Nikhil Garg, Manish Raghavan

CoRR, February, 2026

2025

What Does Your Benchmark Really Measure? A Framework for Robust Inference of AI Capabilities.

Nathanael Jo, Ashia Wilson

CoRR, September, 2025

Homogeneous Algorithms Can Reduce Competition in Personalized Pricing.

Nathanael Jo, Ashia C. Wilson, Kathleen Creel, Manish Raghavan

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features.

Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. Ho

Proceedings of the IEEE Conference on Secure and Trustworthy Machine Learning, 2024

2023

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription.

Patrick Vossler, Sina Aghaei, Nathan Justin, Nathanael Jo, Andrés Gómez, Phebe Vayanos

CoRR, 2023

Learning Optimal Fair Decision Trees: Trade-offs Between Interpretability, Fairness, and Accuracy.

Nathanael Jo, Sina Aghaei, Jack Benson, Andrés Gómez, Phebe Vayanos

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results.

Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Learning Optimal Fair Classification Trees.

Nathanael Jo, Sina Aghaei, Jack Benson, Andrés Gómez, Phebe Vayanos

CoRR, 2022

2021

Learning Optimal Prescriptive Trees from Observational Data.

Nathanael Jo, Sina Aghaei, Andrés Gómez, Phebe Vayanos

CoRR, 2021