15 citations · 51 across the 40 of their papers we have counts for
5 papers · 1 filter
Dimensionality Reduction for Wasserstein Barycenter
Zachary Izzo, Sandeep Silwal, Samson Zhou
The Wasserstein barycenter is a geometric construct which captures the notion of centrality among probability distributions, and which has found many applications in machine learni…
Learning-Augmented -means Clustering
Jon C. Ergun, Zhili Feng, Sandeep Silwal +2
-means clustering is a well-studied problem due to its wide applicability. Unfortunately, there exist strong theoretical limits on the performance of any algorithm for the -m…
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering
Shyam Narayanan, Sandeep Silwal, Piotr Indyk +1
Random dimensionality reduction is a versatile tool for speeding up algorithms for high-dimensional problems. We study its application to two clustering problems: the facility loca…
Learning-based Support Estimation in Sublinear Time
Talya Eden, Piotr Indyk, Shyam Narayanan +3
We consider the problem of estimating the number of distinct elements in a large data set (or, equivalently, the support size of the distribution induced by the data set) from a ra…
Adversarial Robustness of Streaming Algorithms through Importance Sampling
Vladimir Braverman, Avinatan Hassidim, Yossi Matias +3
In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more g…