This work addresses the outlier detection problem for feature-evolving streams, which has
not been studied before. In this setting both (1) data points may evolve, with feature values …

Minwise hashing is a fundamental and one of the most successful hashing algorithm in the
literature. Recent advances based on the idea of densification (Shrivastava\& Li, 2014) have …

Many tasks in computer systems could be abstracted as distributing items into buckets, so
that the allocation of items across buckets is as balanced as possible, and furthermore …

We consider the Similarity Sketching problem: Given a universe [u]={0,..., u-1} we want a
random function S map** subsets A of [u] into vectors S (A) of size t, such that similarity is …

Hashing is a basic tool for dimensionality reduction employed in several aspects of machine
learning. However, the perfomance analysis is often carried out under the abstract …

Feature hashing, also known as {\em the hashing trick}, introduced by Weinberger et
al.(2009), is one of the key techniques used in scaling-up machine learning algorithms …

This review considers methods and algorithms for fast estimation of distance/similarity
measures between initial data from vector representations with binary or integer-valued …

In this work we study Invertible Bloom Lookup Tables (IBLTs) with small failure probabilities.
IBLTs are highly versatile data structures that have found applications in set reconciliation …

Randomized algorithms are often enjoyed for their simplicity, but the hash functions
employed to yield the desired probabilistic guarantees are often too complicated to be …

In dynamic load balancing, we wish to distribute balls into bins in an environment where
both balls and bins can be added and removed. We want to minimize the maximum load of …