Search Results - "Suriyakumar, Vinith"

Algorithms that Approximate Data Removal: New Results and Limitations by Suriyakumar, Vinith M, Wilson, Ashia C

“…We study the problem of deleting user data from machine learning models trained using empirical risk minimization. Our focus is on learning algorithms which…”
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Algorithmic Pluralism: A Structural Approach To Equal Opportunity by Jain, Shomik, Suriyakumar, Vinith, Creel, Kathleen, Wilson, Ashia

“…We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic…”
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When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction by Suriyakumar, Vinith M, Ghassemi, Marzyeh, Ustun, Berk

“…Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we…”
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Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models by Suriyakumar, Vinith M, Alur, Rohan, Sekhari, Ayush, Raghavan, Manish, Wilson, Ashia C

“…Text-to-image diffusion models rely on massive, web-scale datasets. Training them from scratch is computationally expensive, and as a result, developers often…”
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Architecture-Level Modeling of Photonic Deep Neural Network Accelerators by Andrulis, Tanner, Chaudhry, Gohar Irfan, Suriyakumar, Vinith M, Emer, Joel S, Sze, Vivienne

“…ISPASS 2024 pp. 307-309 Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement…”
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Architecture-Level Modeling of Photonic Deep Neural Network Accelerators by Andrulis, Tanner, Chaudhry, Gohar Irfan, Suriyakumar, Vinith M., Emer, Joel S., Sze, Vivienne

“…Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement energy and it enables…”
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One-shot Empirical Privacy Estimation for Federated Learning by Andrew, Galen, Kairouz, Peter, Oh, Sewoong, Oprea, Alina, McMahan, H. Brendan, Suriyakumar, Vinith M

“…Privacy estimation techniques for differentially private (DP) algorithms are useful for comparing against analytical bounds, or to empirically measure privacy…”
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Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings by Suriyakumar, Vinith M, Papernot, Nicolas, Goldenberg, Anna, Ghassemi, Marzyeh

“…Machine learning models in health care are often deployed in settings where it is important to protect patient privacy. In such settings, methods for…”
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3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI by Chang, Alex, Suriyakumar, Vinith, Moturu, Abhishek, Tu, James, Tewattanarat, Nipaporn, Joshi, Sayali, Doria, Andrea, Goldenberg, Anna

“…Modern deep unsupervised learning methods have shown great promise for detecting diseases across a variety of medical imaging modalities. While previous…”
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Private Multi-Winner Voting for Machine Learning by Dziedzic, Adam, Choquette-Choo, Christopher A, Dullerud, Natalie, Suriyakumar, Vinith Menon, Shamsabadi, Ali Shahin, Kaleem, Muhammad Ahmad, Jha, Somesh, Papernot, Nicolas, Wang, Xiao

“…Private multi-winner voting is the task of revealing $k$-hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been…”
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Public Data-Assisted Mirror Descent for Private Model Training by Amid, Ehsan, Ganesh, Arun, Mathews, Rajiv, Ramaswamy, Swaroop, Song, Shuang, Steinke, Thomas, Suriyakumar, Vinith M, Thakkar, Om, Thakurta, Abhradeep

“…In this paper, we revisit the problem of using in-distribution public data to improve the privacy/utility trade-offs for differentially private (DP) model…”
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Using Generative Models for Pediatric wbMRI by Chang, Alex, Suriyakumar, Vinith M, Moturu, Abhishek, Tewattanarat, Nipaporn, Doria, Andrea, Goldenberg, Anna

“…Early detection of cancer is key to a good prognosis and requires frequent testing, especially in pediatrics. Whole-body magnetic resonance imaging (wbMRI) is…”
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