Vinith M. Suriyakumar

According to our database, Vinith M. Suriyakumar authored at least 15 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models.

Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari, Manish Raghavan, Ashia C. Wilson

CoRR, 2024

Architecture-Level Modeling of Photonic Deep Neural Network Accelerators.

Tanner Andrulis, Gohar Irfan Chaudhry, Vinith M. Suriyakumar, Joel S. Emer, Vivienne Sze

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2024

One-shot Empirical Privacy Estimation for Federated Learning.

Galen Andrew, Peter Kairouz, Sewoong Oh, Alina Oprea, Hugh Brendan McMahan, Vinith Menon Suriyakumar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Algorithmic Pluralism: A Structural Approach To Equal Opportunity.

Shomik Jain, Vinith M. Suriyakumar, Kathleen Creel, Ashia Wilson

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

Private Multi-Winner Voting for Machine Learning.

Adam Dziedzic, Christopher A. Choquette-Choo, Natalie Dullerud, Vinith M. Suriyakumar, Ali Shahin Shamsabadi, Muhammad Ahmad Kaleem, Somesh Jha, Nicolas Papernot, Xiao Wang

Proc. Priv. Enhancing Technol., January, 2023

Algorithmic Pluralism: A Structural Approach Towards Equal Opportunity.

Shomik Jain, Vinith M. Suriyakumar, Ashia Wilson

CoRR, 2023

When Personalization Harms Performance: Reconsidering the Use of Group Attributes in Prediction.

Vinith Menon Suriyakumar, Marzyeh Ghassemi, Berk Ustun

Proceedings of the International Conference on Machine Learning, 2023

2022

When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction.

Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun

CoRR, 2022

Algorithms that Approximate Data Removal: New Results and Limitations.

Vinith M. Suriyakumar, Ashia C. Wilson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Public Data-Assisted Mirror Descent for Private Model Training.

Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Vinith M. Suriyakumar, Om Thakkar, Abhradeep Thakurta

Proceedings of the International Conference on Machine Learning, 2022

2021

3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI.

Alex Chang, Vinith M. Suriyakumar, Abhishek Moturu, James Tu, Nipaporn Tewattanarat, Sayali Joshi, Andrea Doria, Anna Goldenberg

CoRR, 2021

Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings.

Vinith M. Suriyakumar, Nicolas Papernot, Anna Goldenberg, Marzyeh Ghassemi

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Can You Fake It Until You Make It?: Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness.

Victoria Cheng, Vinith M. Suriyakumar, Natalie Dullerud, Shalmali Joshi, Marzyeh Ghassemi

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

2020

Using Generative Models for Pediatric wbMRI.

Alex Chang, Vinith M. Suriyakumar, Abhishek Moturu, Nipaporn Tewattanarat, Andrea Doria, Anna Goldenberg

CoRR, 2020

2017

Open-source software for collision detection in external beam radiation therapy.

Vinith M. Suriyakumar, Renee Xu, Csaba Pinter, Gabor Fichtinger

Proceedings of the Medical Imaging 2017: Image-Guided Procedures, 2017