Instance-Adaptive Online Multicalibration, Submitted to NeurIPS 2026

with Zhiming Huang, Jamie Morgenstern, and Aaron Roth

https://arxiv.org/pdf/2605.09273

When Empowerment Disempowers, Submitted to NeurIPS 2026

with Claire Yang, Maya Cakmak and Max Kleiman-Weiner

Certified Robust Regression via Randomized Smoothing, ICML 2026

with Natalie Frank

Welfare-Centric Clustering, AISTATS 2026

with Seyed A. Esmaeili, and Jamie Morgenstern

https://arxiv.org/abs/2508.10345

Efficient Uncoupled Learning Dynamics with $\widetilde{O}(T^{-1/4})$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback, AISTATS 2026

with Arnab Maiti, Ioannis Panageas, Jamie Morgenstsern, Lillian J. Ratliff, and Kevin Jamieson

https://arxiv.org/pdf/2602.21436

Fair Clustering: Critique, Caveats, and Future Directions, IEEE SaTML 2025

with John Dickerson, Seyed A Esmaeili and Jamie Morgenstern

https://arxiv.org/pdf/2406.15960

Doubly Constrained Fair Clustering, NeurIPS 2023

with John Dickerson, Seyed A Esmaeili and Jamie Morgenstern

https://arxiv.org/pdf/2305.19475

Active Sampling for Min-Max Fairness, ICML 2022

with Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern and Chris Russel

https://arxiv.org/pdf/2006.06879

Adaptive Sampling to Reduce Disparate Performance, preprint

with Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner and Jamie Morgenstern

https://arxiv.org/pdf/2006.06879v1

Robust Regression with Missing Feature, In Submission

with Jamie Morgenstern

Machine Teaching by Rule with Statistical Queries, preprint

with Romain Camilleri, Kevin Jamieson and Jamie Morgenstern