On the Compatibility of Privacy and Fairness. - Rachel Cummings; Varun Gupta 006; Dhamma Kimpara; Jamie Morgenstern (UMAP (Adjunct Publication), 2019)

Adaptive Machine Unlearning. - Varun Gupta 006; Christopher Jung 001; Seth Neel; Aaron Roth 001; Saeed Sharifi-Malvajerdi; Chris Waites (NeurIPS, 2021)

Online Multivalid Learning: Means, Moments, and Prediction Intervals. - Varun Gupta 006; Christopher Jung 001; Georgy Noarov; Mallesh M. Pai; Aaron Roth 001 (ITCS, 2022)

Practical Adversarial Multivalid Conformal Prediction. - Osbert Bastani; Varun Gupta 006; Christopher Jung 001; Georgy Noarov; Ramya Ramalingam; Aaron Roth 001 (NeurIPS, 2022)

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

Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. - Natalie Collina; Varun Gupta 006; Aaron Roth 001 (EC, 2024)

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

Tractable Agreement Protocols. - Natalie Collina; Surbhi Goel; Varun Gupta 006; Aaron Roth 001 (STOC, 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)