175 citations · 262 across the 24 of their papers we have counts for
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cs.LG2023
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Homa Esfahanizadeh, Adam Yala, Rafael G. L. D'Oliveira +8
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice. A promising technique…
cs.LG2022★ 13 cited
Planting Undetectable Backdoors in Machine Learning Models
Shafi Goldwasser, Michael P. Kim, Vinod Vaikuntanathan +1
Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicio…
cs.LG2021
The Fine-Grained Hardness of Sparse Linear Regression
Aparna Gupte, Vinod Vaikuntanathan
Sparse linear regression is the well-studied inference problem where one is given a design matrix and a response vector $\mathbf{b} \in \mat…