publications

Conferences

One-Shot Empirical Privacy Estimation for Federated Learning
Andrew, G., P. Kairouz, S. Oh, A. Opera, H. B. McMahan, V.M. Suriyakumar. ICLR 2024. (Oral, Top 1% of submissions)

Algorithmic Pluralism: A Structural Approach Towards Equal Opportunity
Jain, S., V.M. Suriyakumar, K. Creel, A. Wilson. EAAMO 2023.

When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
Suriyakumar, V.M., M. Ghassemi*, B. Ustun*. ICML 2023. (Oral, Top 3% of submissions) * denotes equal supervision

Algorithms that Approximate Data Removal: New Results and Limitations
Suriyakumar, V.M., A. Wilson. NeurIPS 2022.

Private Multi-Winner Voting for Machine Learning
Dziedzic, A., C.A. Choquette-Choo*, N.Dullerud*, V.M. Suriyakumar*, A.S. Shamsabadi, N. Papernot, S. Jha, X. Wang. PETS 2023. * denotes equal contribution

Public Data-Assisted Mirror Descent for Private Model Training
Amid, E., A. Ganesh, R. Matthews, S. Ramaswamy, S. Song, T. Steinke, V.M. Suriyakumar, O. Thakkar, A. Thakurta. ICML 2022. (alphabetical order)

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings
ACM FAccT 2021
Suriyakumar, V.M., N. Papernot, A. Goldenberg, M. Ghassemi. 2020.

Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness
ACM FAccT 2021
Cheng, V., V.M. Suriyakumar, N. Dullerud, S. Joshi, M. Ghassemi. 2020.

Using Generative Models for Pediatric wbMRI
Medical Imaging in Deep Learning 2020
Chang. A*, V.M. Suriyakumar *, A. Moturu*, N. Tewattanarat, A. Doria, and A. Goldenberg. 2020. * denotes equal contribution.

Preprints

Implications of Algorithmic Stability for Distributional Shift
Hulkund, N., V.M. Suriyakumar, T. Killian, M. Ghassemi. 2022.

Books and Book Chapters

Differential Privacy and Medical Data Analysis
Differential Privacy and Artificial Intelligence: Theory and Practice Now Publisher Inc.
Suriyakumar, V.M., N. Papernot, A. Goldenberg, and M. Ghassemi. 2024. (Forthcoming)