Search Results for author: Elchanan Mossel

Found 27 papers, 3 papers with code

Reconstruction on Trees and Low-Degree Polynomials

no code implementations14 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.

Community Detection

Spoofing Generalization: When Can't You Trust Proprietary Models?

no code implementations15 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.

Opinion Dynamics under Social Pressure

no code implementations22 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.

Learning to Sample from Censored Markov Random Fields

no code implementations15 Jan 2021 Ankur Moitra, Elchanan Mossel, Colin Sandon

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

Probabilistic Aspects of Voting, Intransitivity and Manipulation

no code implementations18 Dec 2020 Elchanan Mossel

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

Probability

Efficient Reconstruction of Stochastic Pedigrees

no code implementations8 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.

Robust testing of low-dimensional functions

no code implementations24 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 $s$.

Model Compression

Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation

no code implementations24 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.

Community Detection

Seeding with Costly Network Information

1 code implementation10 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

Long ties accelerate noisy threshold-based contagions

1 code implementation8 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

Seeded Graph Matching via Large Neighborhood Statistics

no code implementations26 Jul 2018 Elchanan Mossel, Jiaming Xu

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

Graph Matching

Contextual Stochastic Block Models

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.

Learning Restricted Boltzmann Machines via Influence Maximization

no code implementations25 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.

Collaborative Filtering Dimensionality Reduction

The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity

no code implementations16 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.

The Vertex Sample Complexity of Free Energy is Polynomial

no code implementations16 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.

Approximating Partition Functions in Constant Time

no code implementations5 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.

Coalescent-based species tree estimation: a stochastic Farris transform

no code implementations13 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.

Bayesian Decision Making in Groups is Hard

no code implementations12 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.

Decision Making

Deep Learning and Hierarchal Generative Models

no code implementations29 Dec 2016 Elchanan Mossel

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

Density Evolution in the Degree-correlated Stochastic Block Model

no code implementations10 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.

Stochastic Block Model

Local Algorithms for Block Models with Side Information

no code implementations10 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.

Community Detection Stochastic Block Model

On the Impossibility of Learning the Missing Mass

no code implementations12 Mar 2015 Elchanan Mossel, Mesrob I. Ohannessian

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

MCMC Learning

no code implementations13 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.

Efficient Bayesian Learning in Social Networks with Gaussian Estimators

no code implementations3 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.

On the Submodularity of Influence in Social Networks

no code implementations2 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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