13 citations · 23 across the 4 of their papers we have counts for
4 papers
Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs
Michael B. Cohen, Jonathan Kelner, John Peebles +4
In this paper we introduce a notion of spectral approximation for directed graphs. While there are many potential ways one might define approximation for directed graphs, most of t…
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More
Michael B. Cohen, Jon Kelner, John Peebles +3
In this paper, we provide faster algorithms for computing various fundamental quantities associated with random walks on a directed graph, including the stationary distribution, pe…
Row Sampling by Lewis Weights
Michael B. Cohen, Richard Peng
We give a simple algorithm to efficiently sample the rows of a matrix while preserving the p-norms of its product with vectors. Given an -by- matrix …
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
Michael B. Cohen, Sam Elder, Cameron Musco +2
We show how to approximate a data matrix with a much smaller sketch that can be used to solve a general class of constrained k-rank approximation p…