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cs.IT2023
Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements
Cullen Haselby, Mark A. Iwen, Deanna Needell +2
In this paper we consider the problem of recovering a low-rank Tucker approximation to a massive tensor based solely on structured random compressive measurements. Crucially, the p…
cs.LG2023
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
Erin George, Michael Murray, William Swartworth +1
We study benign overfitting in two-layer ReLU networks trained using gradient descent and hinge loss on noisy data for binary classification. In particular, we consider linearly se…
cs.DS2023
Optimal Eigenvalue Approximation via Sketching
William Swartworth, David P. Woodruff
Given a symmetric matrix , we show from the simple sketch , where is a Gaussian matrix with rows, that there is a procedure for approximating all eigen…