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Differentially Private Synthetic Data Using KD-Trees
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge.
Efficient and Local Parallel Random Walks
Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning.
Scaling up Kernel Ridge Regression via Locality Sensitive Hashing
Random binning features, introduced in the seminal paper of Rahimi and Recht (2007), are an efficient method for approximating a kernel matrix using locality sensitive hashing.
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