Large-scale distributed training of neural networks is often limited by network bandwidth,
wherein the communication time overwhelms the local computation time. Motivated by the …

Approximate Membership Query (AMQ) data structures, such as the Bloom filter, quotient
filter, and cuckoo filter, have found numerous applications in databases, storage systems …

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

We present JetStream, a system that allows real-time analysis of large, widely-distributed
changing data sets. Traditional approaches to distributed analytics require users to specify …

We describe a new algorithm called FrequentDirections for deterministic matrix sketching in
the row-update model. The algorithm is presented an arbitrary input matrix A ∈ R^ n * d one …

This paper resolves one of the longest standing basic problems in the streaming
computational model. Namely, optimal construction of quantile sketches. An ε approximate …

This paper describes a reference architecture for self-maintaining systems that can learn
continually, as data arrives. In environments where data evolves, we need architectures that …

In this paper we consider efficient construction of" composable core-sets" for basic diversity
and coverage maximization problems. A core-set for a point-set in a metric space is a subset …

Summary statistics such as the mean and variance are easily maintained for large,
distributed data streams, but order statistics (ie, sample quantiles) can only be approximately …

An effective technique for solving optimization problems over massive data sets is to
partition the data into smaller pieces, solve the problem on each piece and compute a …