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no code implementations • 14 Sep 2021 • Frederic Koehler, Elchanan Mossel

Notably, the celebrated Belief Propagation (BP) algorithm achieves Bayes-optimal performance for the reconstruction problem of predicting the value of the Markov process at the root of the tree from its values at the leaves.

no code implementations • 15 Jun 2021 • Ankur Moitra, Elchanan Mossel, Colin Sandon

In this work, we study the computational complexity of determining whether a machine learning model that perfectly fits the training data will generalizes to unseen data.

no code implementations • 22 Apr 2021 • Ali Jadbabaie, Anuran Makur, Elchanan Mossel, Rabih Salhab

At each time step, agents broadcast their declared opinions on a social network, which are governed by the agents' inherent opinions and social pressure.

no code implementations • 15 Jan 2021 • Ankur Moitra, Elchanan Mossel, Colin Sandon

These are Markov Random Fields where some of the nodes are censored (not observed).

no code implementations • 18 Dec 2020 • Elchanan Mossel

These lecture notes are based on lectures given in 2019 Saint-Flour Probability School.

Probability

no code implementations • 8 May 2020 • Younhun Kim, Elchanan Mossel, Govind Ramnarayan, Paxton Turner

We introduce a new algorithm called {\sc Rec-Gen} for reconstructing the genealogy or \textit{pedigree} of an extant population purely from its genetic data.

no code implementations • 24 Apr 2020 • Anindya De, Elchanan Mossel, Joe Neeman

Using our techniques, we also obtain a fully noise tolerant tester with the same query complexity for any class $\mathcal{C}$ of linear $k$-juntas with surface area bounded by $s$.

no code implementations • 24 May 2019 • Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel

The analysis of Belief Propagation and other algorithms for the {\em reconstruction problem} plays a key role in the analysis of community detection in inference on graphs, phylogenetic reconstruction in bioinformatics, and the cavity method in statistical physics.

1 code implementation • 10 May 2019 • Dean Eckles, Hossein Esfandiari, Elchanan Mossel, M. Amin Rahimian

We provide an approximation algorithm with a tight $(1-1/e){\mbox{OPT}}-\epsilon n$ guarantee, using $O_{\epsilon}(k^2\log n)$ influence samples and show that this dependence on $k$ is asymptotically optimal.

Social and Information Networks Computational Complexity Probability Physics and Society

1 code implementation • 8 Oct 2018 • Dean Eckles, Elchanan Mossel, M. Amin Rahimian, Subhabrata Sen

In widely-used models of biological contagion, interventions that randomly rewire edges (generally making them "longer") accelerate spread.

Social and Information Networks Probability Physics and Society 91D30, 05C80

no code implementations • 26 Jul 2018 • Elchanan Mossel, Jiaming Xu

We study a well known noisy model of the graph isomorphism problem.

no code implementations • NeurIPS 2018 • Yash Deshpande, Andrea Montanari, Elchanan Mossel, Subhabrata Sen

We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities.

no code implementations • 25 May 2018 • Guy Bresler, Frederic Koehler, Ankur Moitra, Elchanan Mossel

This hardness result is based on a sharp and surprising characterization of the representational power of bounded degree RBMs: the distribution on their observed variables can simulate any bounded order MRF.

no code implementations • 16 Feb 2018 • Vishesh Jain, Frederic Koehler, Elchanan Mossel

The mean field approximation to the Ising model is a canonical variational tool that is used for analysis and inference in Ising models.

no code implementations • 16 Feb 2018 • Vishesh Jain, Frederic Koehler, Elchanan Mossel

Results in graph limit literature by Borgs, Chayes, Lov\'asz, S\'os, and Vesztergombi show that for Ising models on $n$ nodes and interactions of strength $\Theta(1/n)$, an $\epsilon$ approximation to $\log Z_n / n$ can be achieved by sampling a randomly induced model on $2^{O(1/\epsilon^2)}$ nodes.

no code implementations • 5 Nov 2017 • Vishesh Jain, Frederic Koehler, Elchanan Mossel

One exception is recent results by Risteski (2016) who considered dense graphical models and showed that using variational methods, it is possible to find an $O(\epsilon n)$ additive approximation to the log partition function in time $n^{O(1/\epsilon^2)}$ even in a regime where correlation decay does not hold.

no code implementations • 13 Jul 2017 • Gautam Dasarathy, Elchanan Mossel, Robert Nowak, Sebastien Roch

As a corollary, we also obtain a new identifiability result of independent interest: for any species tree with $n \geq 3$ species, the rooted species tree can be identified from the distribution of its unrooted weighted gene trees even in the absence of a molecular clock.

no code implementations • 12 May 2017 • Jan Hązła, Ali Jadbabaie, Elchanan Mossel, M. Amin Rahimian

We study the computations that Bayesian agents undertake when exchanging opinions over a network.

no code implementations • 29 Dec 2016 • Elchanan Mossel

It is argued that deep learning is efficient for data that is generated from hierarchal generative models.

no code implementations • 10 Sep 2015 • Elchanan Mossel, Jiaming Xu

There is a recent surge of interest in identifying the sharp recovery thresholds for cluster recovery under the stochastic block model.

no code implementations • 10 Aug 2015 • Elchanan Mossel, Jiaming Xu

There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs.

no code implementations • 21 Apr 2015 • Elchanan Mossel, Sebastien Roch

We consider the reconstruction of a phylogeny from multiple genes under the multispecies coalescent.

no code implementations • 12 Mar 2015 • Elchanan Mossel, Mesrob I. Ohannessian

The probability of this event is referred to as the "missing mass".

no code implementations • 13 Jul 2013 • Varun Kanade, Elchanan Mossel

The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas.

1 code implementation • 24 Jun 2013 • Florent Krzakala, Cristopher Moore, Elchanan Mossel, Joe Neeman, Allan Sly, Lenka Zdeborová, Pan Zhang

Spectral algorithms are classic approaches to clustering and community detection in networks.

no code implementations • 3 Feb 2010 • Elchanan Mossel, Noah Olsman, Omer Tamuz

Finally, we show that on trees and on distance transitive-graphs the process converges after $D$ steps, and that it preserves privacy, so that agents learn very little about the private signal of most other agents, despite the efficient aggregation of information.

no code implementations • 2 Dec 2006 • Elchanan Mossel, Sebastien Roch

Here we prove a conjecture of KKT: we show that the function $\sigma(S)$ is submodular under the assumptions above.

Probability Computer Science and Game Theory Social and Information Networks

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