34 citations · 269 across the 42 of their papers we have counts for
6 papers · 1 filter
Introduction to Coresets: Approximated Mean
Alaa Maalouf, Ibrahim Jubran, Dan Feldman
A \emph{strong coreset} for the mean queries of a set in is a small weighted subset , which provably approximates its sum of squared distances to…
A Unified Approach to Coreset Learning
Alaa Maalouf, Gilad Eini, Ben Mussay +2
Coreset of a given dataset and loss function is usually a small weighed set that approximates this loss for every query from a given set of queries. Coresets have shown to be very…
Coresets for Decision Trees of Signals
Ibrahim Jubran, Ernesto Evgeniy Sanches Shayda, Ilan Newman +1
A -decision tree (or -tree) is a recursive partition of a matrix (2D-signal) into block matrices (axis-parallel rectangles, leaves) where each rectangle is assi…
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
Lucas Liebenwein, Alaa Maalouf, Oren Gal +2
We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultane…
Low-Regret Active learning
Cenk Baykal, Lucas Liebenwein, Dan Feldman +1
We develop an online learning algorithm for identifying unlabeled data points that are most informative for training (i.e., active learning). By formulating the active learning pro…
Provably Approximated ICP
Ibrahim Jubran, Alaa Maalouf, Ron Kimmel +1
The goal of the \emph{alignment problem} is to align a (given) point cloud to another (observed) point cloud . That is, to compute…