1 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
Dimension Reduction for Clustering: The Curious Case of Discrete Centers
Shaofeng H. -C. Jiang, Robert Krauthgamer, Shay Sapir +2
The Johnson-Lindenstrauss transform is a fundamental method for dimension reduction in Euclidean spaces, that can map any dataset of points into dimension with low…
On the Adversarial Robustness of Online Importance Sampling
Yotam Kenneth-Mordoch, Shay Sapir
This paper studies the adversarial-robustness of importance-sampling (aka sensitivity sampling); a useful algorithmic technique that samples elements with probabilities proportiona…
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
Jie Gao, Rajesh Jayaram, Benedikt Kolbe +4
Randomized dimensionality reduction is a widely-used algorithmic technique for speeding up large-scale Euclidean optimization problems. In this paper, we study dimension reduction…
The Power of Recursive Embeddings for Metrics
Robert Krauthgamer, Nir Petruschka, Shay Sapir
Metric embedding is a powerful tool used extensively in mathematics and computer science. We devise a new method of using metric embeddings recursively, which turns out to be parti…