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20002026
most citedSpectral redemption: clustering sparse networks

658 citations · 2.2k across the 187 of their papers we have counts for

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Showing 2005Show all

6 papers · 1 filter

math.PR2005★ 1 cited

Slow Emergence of Cooperation for Win-Stay Lose-Shift on Trees

Elchanan Mossel, Sebastien Roch

We consider a group of agents on a graph who repeatedly play the prisoner's dilemma game against their neighbors. The players adapt their actions to the past behavior of their oppo…

math.PR2005★ 2 cited

Evolutionary Trees and the Ising Model on the Bethe Lattice: a Proof of Steel's Conjecture

Constantinos Daskalakis, Elchanan Mossel, Sebastien Roch

A major task of evolutionary biology is the reconstruction of phylogenetic trees from molecular data. The evolutionary model is given by a Markov chain on a tree. Given samples fro…

q-bio.PE2005★ 51 cited

Limitations of Markov chain Monte Carlo algorithms for Bayesian Inference of phylogeny

Elchanan Mossel, Eric Vigoda

Markov chain Monte Carlo algorithms play a key role in the Bayesian approach to phylogenetic inference. In this paper, we present the first theoretical work analyzing the rate of c…

cs.CC2005

Conditional Hardness for Approximate Coloring

Irit Dinur, Elchanan Mossel, Oded Regev

We study the coloring problem: Given a graph G, decide whether or , where c(G) is the chromatic number of G. We derive conditional hardness for this probl…

math.PR2005★ 39 cited

Noise stability of functions with low influences: invariance and optimality

Elchanan Mossel, Ryan O'Donnell, Krzysztof Oleszkiewicz

In this paper we study functions with low influences on product probability spaces. The analysis of boolean functions with low influences has become a central problem in discrete F…

cs.LG2005★ 29 cited

Learning nonsingular phylogenies and hidden Markov models

Elchanan Mossel, Sébastien Roch

In this paper we study the problem of learning phylogenies and hidden Markov models. We call a Markov model nonsingular if all transition matrices have determinants bounded away fr…