Given a set of $ n $ points in $ d $ dimensions, the Euclidean $ k $-means problem (resp.
Euclidean $ k $-median) consists of finding $ k $ centers such that the sum of squared …

The (k, z)-clustering problem consists of finding a set of k points called centers, such that the
sum of distances raised to the power of z of every data point to its closest center is …

We investigate coresets-succinct, small summaries of large data sets-so that solutions found
on the summary are provably competitive with solution found on the full data set. We provide …

Geometric data summarization has become an essential tool in both geometric
approximation algorithms and where geometry intersects with big data problems. In linear or …

Random projection (RP) is a classical technique for reducing storage and computational
costs. We analyze RP-based approximations of convex programs, in which the original …

There is an arms race in the data management industry to support statistical analytics.
Feature selection, the process of selecting a feature set that will be used to build a statistical …

We perform the first study of the tradeoff space of access methods and replication to support
statistical analytics using first-order methods executed in the main memory of a Non-Uniform …

Several recent randomized linear algebra algorithms rely upon fast dimension reduction
methods. A popular choice is the subsampled randomized Hadamard transform (SRHT). In …

Vertical federated learning (VFL), where data features are stored in multiple parties
distributively, is an important area in machine learning. However, the communication …

Many pressing questions in science are macroscopic: they require scientists to consult
information expressed in a wide range of resources, many of which are not organized in a …