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no code implementations • 19 Oct 2021 • Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer

Differentially private algorithms for common metric aggregation tasks, such as clustering or averaging, often have limited practicality due to their complexity or a large number of data points that is required for accurate results.

no code implementations • 30 Jul 2021 • Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer

We propose a novel framework for adversarial streaming that hybrids two recently suggested frameworks by Hassidim et al. (2020) and by Woodruff and Zhou (2021).

no code implementations • 25 Oct 2020 • Edith Cohen, Ofir Geri, Tamas Sarlos, Uri Stemmer

A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query statistics.

no code implementations • NeurIPS 2020 • Edith Cohen, Rasmus Pagh, David P. Woodruff

We design novel composable sketches for WOR $\ell_p$ sampling, weighted sampling of keys according to a power $p\in[0, 2]$ of their frequency (or for signed data, sum of updates).

no code implementations • 31 May 2020 • Eliav Buchnik, Edith Cohen

Classically, ML models trained with stochastic gradient descent (SGD) are designed to minimize the average loss per example and use a distribution of training examples that remains {\em static} in the course of training.

no code implementations • 31 Jul 2019 • Gal Sadeh, Edith Cohen, Haim Kaplan

Our main result is a surprising upper bound of $O( s \tau \epsilon^{-2} \ln \frac{n}{\delta})$ for a broad class of models that includes IC and LT models and their mixtures, where $n$ is the number of nodes and $\tau$ is the number of diffusion steps.

no code implementations • ICLR 2019 • Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias

We make a principled argument for the properties of our arrangements that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal distribution of training examples.

no code implementations • ICLR 2019 • Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias

Optimization of machine learning models is commonly performed through stochastic gradient updates on randomly ordered training examples.

no code implementations • 12 Jun 2017 • Edith Cohen, Shiri Chechik, Haim Kaplan

At the core of our design is the {\em one2all} construction of multi-objective probability-proportional-to-size (pps) samples: Given a set $M$ of centroids and $\alpha \geq 1$, one2all efficiently assigns probabilities to points so that the clustering cost of {\em each} $Q$ with cost $V(Q) \geq V(M)/\alpha$ can be estimated well from a sample of size $O(\alpha |M|\epsilon^{-2})$.

no code implementations • 7 Mar 2017 • Eliav Buchnik, Edith Cohen

Classic methods capture the graph structure through some underlying diffusion process that propagates through the graph edges.

no code implementations • 30 Mar 2016 • Edith Cohen

Inspired by the success of social influence as an alternative to spectral centrality such as Page Rank, we explore SSL with our kernels and develop highly scalable algorithms for parameter setting, label learning, and sampling.

no code implementations • 30 Mar 2015 • Shiri Chechik, Edith Cohen, Haim Kaplan

The estimate is based on a weighted sample of $O(\epsilon^{-2})$ pairs of points, which is computed using $O(n)$ distance computations.

no code implementations • 26 Aug 2014 • Edith Cohen, Daniel Delling, Thomas Pajor, Renato F. Werneck

The gold standard for Influence Maximization is the greedy algorithm, which iteratively adds to the seed set a node maximizing the marginal gain in influence.

Data Structures and Algorithms Social and Information Networks G.2.2; H.2.8

no code implementations • 14 Jun 2013 • Edith Cohen

We present the Historic Inverse Probability (HIP) estimators which are applied to the ADS of a node to estimate a large natural class of statistics.

Data Structures and Algorithms Social and Information Networks

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