The last few years have seen a surge of work on high dimensional statist...
We initiate the study of active learning polynomial threshold functions
...
Estimating the quantiles of a large dataset is a fundamental problem in ...
Sampling random nodes is a fundamental algorithmic primitive in the anal...
A knowledge graph (KG) is a data structure which represents entities and...
A streaming algorithm is adversarially robust if it is guaranteed to per...
We study the process of information dispersal in a network with communic...
Nowadays, as cameras are rapidly adopted in our daily routine, images of...
Laws of large numbers guarantee that given a large enough sample from so...
We investigate the adversarial robustness of streaming algorithms. In th...
We investigate adaptive sublinear algorithms for detecting monotone patt...
We study the problem of finding monotone subsequences in an array from t...
We show that there exist properties that are maximally hard for testing,...
Layout is a fundamental component of any graphic design. Creating large
...
Semi-random processes involve an adaptive decision-maker, whose goal is ...
Random sampling is a fundamental primitive in modern algorithms, statist...
Consider the following hat guessing game: n players are placed on n
vert...
We study testing of local properties in one-dimensional and multi-dimens...
The emerging theory of graph limits exhibits an interesting analytic
per...
One of the main challenges in property testing is to characterize those
...
A sequence f{1,...,n}→R contains a permutation π
of length k if there ex...
Understanding the local behaviour of structured multi-dimensional data i...