540 citations · 1.1k across the 26 of their papers we have counts for
27 papers
Quantifying Structure in CLIP Embeddings: A Statistical Framework for Concept Interpretation
Jitian Zhao, Chenghui Li, Frederic Sala +1
Concept-based approaches, which aim to identify human-understandable concepts within a model's internal representations, are a promising method for interpreting embeddings from dee…
Co-factor analysis of citation networks
Alex Hayes, Karl Rohe
One compelling use of citation networks is to characterize papers by their relationships to the surrounding literature. We propose a method to characterize papers by embedding them…
Estimating Graph Dimension with Cross-validated Eigenvalues
Fan Chen, Sebastien Roch, Karl Rohe +1
In applied multivariate statistics, estimating the number of latent dimensions or the number of clusters, , is a fundamental and recurring problem. We study a sequence of statis…
A New Basis for Sparse Principal Component Analysis
Fan Chen, Karl Rohe
Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a matrix) is approximately sparse. We propose a method that presumes…
Vintage Factor Analysis with Varimax Performs Statistical Inference
Karl Rohe, Muzhe Zeng
Psychologists developed Multiple Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors. In…
Targeted sampling from massive block model graphs with personalized PageRank
Fan Chen, Yini Zhang, Karl Rohe
The paper provides statistical theory and intuition for personalized PageRank (called "PPR"): a popular technique that samples a small community from a massive network. We study a…
Reducing Seed Bias in Respondent-Driven Sampling by Estimating Block Transition Probabilities
Yilin Zhang, Karl Rohe, Sebastien Roch
Respondent-driven sampling (RDS) is a popular approach to study marginalized or hard-to-reach populations. It collects samples from a networked population by incentivizing particip…
Asymptotic Seed Bias in Respondent-driven Sampling
Yuling Yan, Bret Hanlon, Sebastien Roch +1
Respondent-driven sampling (RDS) collects a sample of individuals in a networked population by incentivizing the sampled individuals to refer their contacts into the sample. This i…
Understanding Regularized Spectral Clustering via Graph Conductance
Yilin Zhang, Karl Rohe
This paper uses the relationship between graph conductance and spectral clustering to study (i) the failures of spectral clustering and (ii) the benefits of regularization. The exp…
Generalized least squares can overcome the critical threshold in respondent-driven sampling
Sebastien Roch, Karl Rohe
In order to sample marginalized and/or hard-to-reach populations, respondent-driven sampling (RDS) and similar techniques reach their participants via peer referral. Under a Markov…
Discovering Political Topics in Facebook Discussion threads with Graph Contextualization
Yilin Zhang, Marie Poux-Berthe, Chris Wells +2
We propose a graph contextualization method, pairGraphText, to study political engagement on Facebook during the 2012 French presidential election. It is a spectral algorithm that…
A note on quickly sampling a sparse matrix with low rank expectation
Karl Rohe, Jun Tao, Xintian Han +1
Given matrices and with positive elements, this paper proposes an algorithm fastRG to sample a sparse matrix with low rank expec…
Novel Sampling Design for Respondent-driven Sampling
Mohammad Khabbazian, Bret Hanlon, Zoe Russek +1
Respondent-driven sampling (RDS) is a method of chain referral sampling popular for sampling hidden and/or marginalized populations. As such, even under the ideal sampling assumpti…
Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion; Some Statistical and Algorithmic Theory for Adaptive-Impute
Juhee Cho, Donggyu Kim, Karl Rohe
Over the past decade, various matrix completion algorithms have been developed. Thresholded singular value decomposition (SVD) is a popular technique in implementing many of them.…
Central limit theorems for network driven sampling
Xiao Li, Karl Rohe
Respondent-Driven Sampling is a popular technique for sampling hidden populations. This paper models Respondent-Driven Sampling as a Markov process indexed by a tree. Our main resu…
Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-observed Low Rank Matrix with Noise
Juhee Cho, Donggyu Kim, Karl Rohe
Matrix completion algorithms recover a low rank matrix from a small fraction of the entries, each entry contaminated with additive errors. In practice, the singular vectors and sin…
Network driven sampling; a critical threshold for design effects
Karl Rohe
Web crawling, snowball sampling, and respondent-driven sampling (RDS) are three types of network sampling techniques used to contact individuals in hard-to-reach populations. This…
A note relating ridge regression and OLS p-values to preconditioned sparse penalized regression
Karl Rohe
When the design matrix has orthonormal columns, "soft thresholding" the ordinary least squares (OLS) solution produces the Lasso solution [Tibshirani, 1996]. If one uses the Puffer…
Covariate-assisted spectral clustering
Norbert Binkiewicz, Joshua T. Vogelstein, Karl Rohe
Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal th…
Discussion of "Estimating the historical and future probabilities of large terrorist events" by Aaron Clauset and Ryan Woodard
Qiurong Cui, Karl Rohe, Zhengjun Zhang
Discussion of "Estimating the historical and future probabilities of large terrorist events" by Aaron Clauset and Ryan Woodard [arXiv:1209.0089].
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
Tai Qin, Karl Rohe
Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently, Chaudhuri et al. (2012) and Amini et al.(2012) proposed inspired variations on the a…
The blessing of transitivity in sparse and stochastic networks
Karl Rohe, Tai Qin
The interaction between transitivity and sparsity, two common features in empirical networks, implies that there are local regions of large sparse networks that are dense. We call…
Preconditioning to comply with the Irrepresentable Condition
Jinzhu Jia, Karl Rohe
Preconditioning is a technique from numerical linear algebra that can accelerate algorithms to solve systems of equations. In this paper, we demonstrate how preconditioning can cir…
The Highest Dimensional Stochastic Blockmodel with a Regularized Estimator
Karl Rohe, Tai Qin, Haoyang Fan
In the high dimensional Stochastic Blockmodel for a random network, the number of clusters (or blocks) K grows with the number of nodes N. Two previous studies have examined the st…