Search Results for author: Edo Liberty

Found 8 papers, 3 papers with code

Streaming Quantiles Algorithms with Small Space and Update Time

1 code implementation29 Jun 2019 Nikita Ivkin, Edo Liberty, Kevin Lang, Zohar Karnin, Vladimir Braverman

Approximating quantiles and distributions over streaming data has been studied for roughly two decades now.

Asymmetric Random Projections

no code implementations22 Jun 2019 Nick Ryder, Zohar Karnin, Edo Liberty

In many applications the data set to be projected is given to us in advance, yet the current RP techniques do not make use of information about the data.

General Classification

Discrepancy, Coresets, and Sketches in Machine Learning

no code implementations11 Jun 2019 Zohar Karnin, Edo Liberty

We provide general techniques for bounding the class discrepancy of machine learning problems.

Density Estimation

ProxQuant: Quantized Neural Networks via Proximal Operators

1 code implementation ICLR 2019 Yu Bai, Yu-Xiang Wang, Edo Liberty

To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights.

Quantization

Optimal Quantile Approximation in Streams

2 code implementations17 Mar 2016 Zohar Karnin, Kevin Lang, Edo Liberty

One of our contributions is a novel representation and modification of the widely used merge-and-reduce construction.

Data Structures and Algorithms

Frequent Directions : Simple and Deterministic Matrix Sketching

no code implementations8 Jan 2015 Mina Ghashami, Edo Liberty, Jeff M. Phillips, David P. Woodruff

It performed $O(d \times \ell)$ operations per row and maintains a sketch matrix $B \in R^{\ell \times d}$ such that for any $k < \ell$ $\|A^TA - B^TB \|_2 \leq \|A - A_k\|_F^2 / (\ell-k)$ and $\|A - \pi_{B_k}(A)\|_F^2 \leq \big(1 + \frac{k}{\ell-k}\big) \|A-A_k\|_F^2 $ .

Data Structures and Algorithms 68W40 (Primary)

An Algorithm for Online K-Means Clustering

no code implementations18 Dec 2014 Edo Liberty, Ram Sriharsha, Maxim Sviridenko

We also show that, experimentally, it is not much worse than k-means++ while operating in a strictly more constrained computational model.

Near-Optimal Entrywise Sampling for Data Matrices

no code implementations NeurIPS 2013 Dimitris Achlioptas, Zohar Karnin, Edo Liberty

We consider the problem of selecting non-zero entries of a matrix $A$ in order to produce a sparse sketch of it, $B$, that minimizes $\|A-B\|_2$.

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