1 citations · 1 across the 9 of their papers we have counts for
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Differentially Private Random Feature Model
Chunyang Liao, Deanna Needell, Hayden Schaeffer +1
Designing privacy-preserving machine learning algorithms has received great attention in recent years, especially in the setting when the data contains sensitive information. Diffe…
Convergence of Manifold Filter-Combine Networks
David R. Johnson, Joyce Chew, Siddharth Viswanath +4
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine framework parallels the popular aggregate-c…
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
Michał Dereziński, Daniel LeJeune, Deanna Needell +1
Despite being a key bottleneck in many machine learning tasks, the cost of solving large linear systems has proven challenging to quantify due to problem-dependent quantities such…
Stochastic gradient descent for streaming linear and rectified linear systems with adversarial corruptions
Halyun Jeong, Deanna Needell, Elizaveta Rebrova
We propose SGD-exp, a stochastic gradient descent approach for linear and ReLU regressions under Massart noise (adversarial semi-random corruption model) for the fully streaming se…