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cs.CG2024
Euclidean distance compression via deep random features
Brett Leroux, Luis Rademacher
Motivated by the problem of compressing point sets into as few bits as possible while maintaining information about approximate distances between points, we construct random nonlin…
stat.ML2024★ 1 cited
On the Nystrom Approximation for Preconditioning in Kernel Machines
Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher +1
Kernel methods are a popular class of nonlinear predictive models in machine learning. Scalable algorithms for learning kernel models need to be iterative in nature, but convergenc…