publications by categories in reversed chronological order.


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.

In Submission

Improving Robustness to Distribution Shift with Algorithmic Stability
Hulkund, N.*, V.M. Suriyakumar*, T. Killian, A. S. Shamsabadi, N. Papernot, M. Ghassemi. 2022. * denotes equal contribution

When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
Suriyakumar, V.M., M. Ghassemi, B. Ustun. 2022. * denotes equal contribution

Books and Book Chapters

Differential Privacy and Medical Data Analysis
Differential Privacy for Artificial Intelligence Applications.
Now Publisher Inc.
Suriyakumar, V.M., N. Papernot, A. Goldenberg, and M. Ghassemi. 2021. (Forthcoming)