44 citations · 199 across the 22 of their papers we have counts for
3 papers · 1 filter
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Raajen Patel, Thomas A. Goldstein, Eva L. Dyer +2
Kernel matrices (e.g. Gram or similarity matrices) are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction. For large dataset…
Self-Expressive Decompositions for Matrix Approximation and Clustering
Eva L. Dyer, Tom A. Goldstein, Raajen Patel +2
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure…
RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets
Azalia Mirhoseini, Eva L. Dyer, Ebrahim. M. Songhori +2
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets. Our f…