
Reconstruction on Trees and LowDegree Polynomials
The study of Markov processes and broadcasting on trees has deep connect...
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In Defense of Fluid Democracy
Fluid democracy is a voting paradigm that allows voters to choose betwee...
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Information Spread with Error Correction
We study the process of information dispersal in a network with communic...
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Spectral Recovery of Binary Censored Block Models
Community detection is the problem of identifying community structure in...
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Spoofing Generalization: When Can't You Trust Proprietary Models?
In this work, we study the computational complexity of determining wheth...
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Approximate polymorphisms
For a function g{0,1}^m→{0,1}, a function f{0,1}^n→{0,1} is called a gp...
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Learning to Sample from Censored Markov Random Fields
We study learning Censor Markov Random Fields (abbreviated CMRFs). These...
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Broadcasting on TwoDimensional Regular Grids
We study a specialization of the problem of broadcasting on directed acy...
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Efficient Reconstruction of Stochastic Pedigrees
We introduce a new algorithm called RecGen for reconstructing the gene...
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A Phase Transition in Arrow's Theorem
Arrow's Theorem concerns a fundamental problem in social choice theory: ...
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Robust testing of lowdimensional functions
A natural problem in highdimensional inference is to decide if a classi...
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AND Testing and Robust Judgement Aggregation
A function f{0,1}^n→{0,1} is called an approximate ANDhomomorphism if c...
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AccuracyMemory Tradeoffs and Phase Transitions in Belief Propagation
The analysis of Belief Propagation and other algorithms for the reconst...
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Seeding with Costly Network Information
The spread of behavior over social networks depends on the contact struc...
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The Circuit Complexity of Inference
Belief propagation is one of the foundations of probabilistic and causal...
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Junta correlation is testable
The problem of tolerant junta testing is a natural and challenging probl...
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How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories
Reconstruction of population histories is a central problem in populatio...
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Broadcasting on Random Directed Acyclic Graphs
We study a generalization of the wellknown model of broadcasting on tre...
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Being Corrupt Requires Being Clever, But Detecting Corruption Doesn't
We consider a variation of the problem of corruption detection on networ...
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Reasoning in Bayesian Opinion Exchange Networks Is PSPACEHard
We study the Bayesian model of opinion exchange of fully rational agents...
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Seeded Graph Matching via Large Neighborhood Statistics
We study a well known noisy model of the graph isomorphism problem. In t...
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Contextual Stochastic Block Models
We provide the first information theoretic tight analysis for inference ...
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Is your function lowdimensional?
We study the problem of testing if a function depends on a small number ...
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Is your data lowdimensional?
We study the problem of testing if a function depends on a small number ...
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Learning Restricted Boltzmann Machines via Influence Maximization
Graphical models are a rich language for describing highdimensional dis...
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Broadcasting on Bounded Degree DAGs
We study the following generalization of the wellknown model of broadca...
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The Vertex Sample Complexity of Free Energy is Polynomial
We study the following question: given a massive Markov random field on ...
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The MeanField Approximation: Information Inequalities, Algorithms, and Complexity
The mean field approximation to the Ising model is a canonical variation...
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Approximating Partition Functions in Constant Time
We study approximations of the partition function of dense graphical mod...
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Bayesian Group Decisions: Algorithms and Complexity
We address the computations that Bayesian agents undertake to realize th...
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Density Evolution in the Degreecorrelated Stochastic Block Model
There is a recent surge of interest in identifying the sharp recovery th...
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Local Algorithms for Block Models with Side Information
There has been a recent interest in understanding the power of local alg...
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On the Impossibility of Learning the Missing Mass
This paper shows that one cannot learn the probability of rare events wi...
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MCMC Learning
The theory of learning under the uniform distribution is rich and deep, ...
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Spectral redemption: clustering sparse networks
Spectral algorithms are classic approaches to clustering and community d...
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Efficient Bayesian Learning in Social Networks with Gaussian Estimators
We consider a group of Bayesian agents who try to estimate a state of th...
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Elchanan Mossel
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Professor of Mathematics at MIT since 2016, Associate Member at Broad Institute since 2016, Core Member at MIT institute for Data, Systems and Society since 2016, Professor at University of Pennsylvania from 20142016, Professor at University of California from 20032016