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Found 34 papers, 3 papers with code
On the Submodularity of Influence in Social Networks
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
Efficient Bayesian Learning in Social Networks with Gaussian Estimators
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.
Spectral redemption: clustering sparse networks
Spectral algorithms are classic approaches to clustering and community detection in networks.
MCMC Learning
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.
On the Impossibility of Learning the Missing Mass
The probability of this event is referred to as the "missing mass".
Distance-based species tree estimation: information-theoretic trade-off between number of loci and sequence length under the coalescent
We consider the reconstruction of a phylogeny from multiple genes under the multispecies coalescent.
Local Algorithms for Block Models with Side Information
There has been a recent interest in understanding the power of local algorithms for optimization and inference problems on sparse graphs.
Density Evolution in the Degree-correlated Stochastic Block Model
There is a recent surge of interest in identifying the sharp recovery thresholds for cluster recovery under the stochastic block model.
Deep Learning and Hierarchal Generative Models
It is argued that deep learning is efficient for data that is generated from hierarchal generative models.
Bayesian Decision Making in Groups is Hard
We study the computations that Bayesian agents undertake when exchanging opinions over a network.
Coalescent-based species tree estimation: a stochastic Farris transform
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.
Approximating Partition Functions in Constant Time
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.
The Vertex Sample Complexity of Free Energy is Polynomial
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.
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity
The mean field approximation to the Ising model is a canonical variational tool that is used for analysis and inference in Ising models.
Learning Restricted Boltzmann Machines via Influence Maximization
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.
Contextual Stochastic Block Models
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.
Seeded Graph Matching via Large Neighborhood Statistics
We study a well known noisy model of the graph isomorphism problem.
Long ties accelerate noisy threshold-based contagions
To model the trade-off between long and short edges we analyze the rate of spread over networks that are the union of circular lattices and random graphs on $n$ nodes.
Social and Information Networks Probability Physics and Society 91D30, 05C80
Seeding with Costly Network Information
We study the task of selecting $k$ nodes, in a social network of size $n$, to seed a diffusion with maximum expected spread size, under the independent cascade model with cascade probability $p$.
Social and Information Networks Computational Complexity Probability Physics and Society
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
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.
Robust testing of low-dimensional functions
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 $s$.
Efficient Reconstruction of Stochastic Pedigrees
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.
Probabilistic Aspects of Voting, Intransitivity and Manipulation
These lecture notes are based on lectures given in 2019 Saint-Flour Probability School.
Probability
Learning to Sample from Censored Markov Random Fields
These are Markov Random Fields where some of the nodes are censored (not observed).
Inference in Opinion Dynamics under Social Pressure
At each time step, agents broadcast their declared opinions on a social network, which are governed by the agents' inherent opinions and social pressure.
Spoofing Generalization: When Can't You Trust Proprietary Models?
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.
Reconstruction on Trees and Low-Degree Polynomials
In this work, we investigate the performance of low-degree polynomials for the reconstruction problem on trees.
Spectral Algorithms Optimally Recover Planted Sub-structures
Spectral algorithms are an important building block in machine learning and graph algorithms.
Efficient Reconstruction of Stochastic Pedigrees: Some Steps From Theory to Practice
They showed that under these conditions if the average number of offspring is a sufficiently large constant, then it is possible to recover a large fraction of the pedigree structure and genetic content by an algorithm they named REC-GEN. We are interested in studying the performance of REC-GEN on simulated data generated according to the model.
A Mathematical Model for Curriculum Learning
Curriculum learning (CL) - training using samples that are generated and presented in a meaningful order - was introduced in the machine learning context around a decade ago.
Errors are Robustly Tamed in Cumulative Knowledge Processes
We study processes of societal knowledge accumulation, where the validity of a new unit of knowledge depends both on the correctness of its derivation and on the validity of the units it depends on.
A Unified Approach to Learning Ising Models: Beyond Independence and Bounded Width
We show that a simple existing approach based on node-wise logistic regression provably succeeds at recovering the underlying model in several new settings where these assumptions are violated: (1) Given dynamically generated data from a wide variety of local Markov chains, like block or round-robin dynamics, logistic regression recovers the parameters with optimal sample complexity up to $\log\log n$ factors.
Reconstructing the Geometry of Random Geometric Graphs
Random geometric graphs are random graph models defined on metric spaces.
Sample-Efficient Linear Regression with Self-Selection Bias
Here, the $\mathbf{x}_{\ell}$ are assumed to be $\mathcal{N}(0, I_n)$ and the noise distribution $\mathbf{\eta}_{\ell}\sim \mathcal{D}$ is centered and independent of $\mathbf{x}_{\ell}$.
