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Are Greedy Task Orderings Better Than Random in Continual Linear Regression?
Matan Tsipory, Ran Levinstein, Itay Evron +3
We analyze task orderings in continual learning for linear regression, assuming joint realizability of training data. We focus on orderings that greedily maximize dissimilarity bet…
Cauchy Random Features for Operator Learning in Sobolev Space
Chunyang Liao, Deanna Needell, Hayden Schaeffer
Operator learning is the approximation of operators between infinite dimensional Banach spaces using machine learning approaches. While most progress in this area has been driven b…
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…
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…