Tutorial: Deploying Secure Multi-Party Computation on the Web Using JIFF. - Kinan Dak Albab; Rawane Issa; Andrei Lapets; Peter Flockhart; Lucy Qin; Ira Globus-Harris (SecDev, 2019)

From Usability to Secure Computing and Back Again. - Lucy Qin; Andrei Lapets; Frederick Jansen; Peter Flockhart; Kinan Dak Albab; Ira Globus-Harris; Shannon Roberts; Mayank Varia (IACR Cryptol. ePrint Arch., 2019)

Improved Differentially Private Analysis of Variance. - Marika Swanberg; Ira Globus-Harris; Iris Griffith; Anna M. Ritz; Adam Groce; Andrew Bray (Proc. Priv. Enhancing Technol., 2019)

Lexicographically Fair Learning: Algorithms and Generalization. - Emily Diana; Wesley Gill; Ira Globus-Harris; Michael Kearns; Aaron Roth 001; Saeed Sharifi-Malvajerdi (FORC, 2021)

Non-parametric Differentially Private Confidence Intervals for the Median. - Jörg Drechsler; Ira Globus-Harris; Audra McMillan; Jayshree Sarathy; Adam D. Smith 001 (CoRR, 2021)

An Algorithmic Framework for Bias Bounties. - Ira Globus-Harris; Michael Kearns; Aaron Roth 001 (FAccT, 2022)

Beyond the Frontier: Fairness Without Accuracy Loss. - Ira Globus-Harris; Michael Kearns; Aaron Roth 001 (CoRR, 2022)

Multicalibrated Regression for Downstream Fairness. - Ira Globus-Harris; Varun Gupta 006; Christopher Jung 001; Michael Kearns; Jamie Morgenstern; Aaron Roth 001 (AIES, 2023)

Multicalibration as Boosting for Regression. - Ira Globus-Harris; Declan Harrison; Michael Kearns; Aaron Roth 001; Jessica Sorrell (ICML, 2023)

Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. - Ira Globus-Harris; Declan Harrison; Michael Kearns; Pietro Perona; Aaron Roth 001 (FAccT, 2024)

Model Ensembling for Constrained Optimization. - Ira Globus-Harris; Varun Gupta 006; Michael Kearns; Aaron Roth 001 (FORC, 2025)

Collaborative Prediction: Tractable Information Aggregation via Agreement. - Natalie Collina; Ira Globus-Harris; Surbhi Goel; Varun Gupta 006; Aaron Roth 001; Mirah Shi (CoRR, 2025)