2 citations · 2 across the 4 of their papers we have counts for
5 papers
Fast Distributed k-Means with a Small Number of Rounds
Tom Hess, Ron Visbord, Sivan Sabato
We propose a new algorithm for k-means clustering in a distributed setting, where the data is distributed across many machines, and a coordinator communicates with these machines t…
A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering
Tom Hess, Michal Moshkovitz, Sivan Sabato
We study k-median clustering under the sequential no-substitution setting. In this setting, a data stream is sequentially observed, and some of the points are selected by the algor…
Sequential no-Substitution k-Median-Clustering
Tom Hess, Sivan Sabato
We study the sample-based k-median clustering objective under a sequential setting without substitutions. In this setting, an i.i.d. sequence of examples is observed. An example ca…
Selecting with History
Tom Hess, Sivan Sabato
We define a new selection problem, \emph{Selecting with History}, which extends the secretary problem to a setting with historical information. We propose a strategy for this probl…
Interactive algorithms: from pool to stream
Sivan Sabato, Tom Hess
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. Interactive algorithms observe suggested elements (representing actions or queries),…