Karl Rohe

Spectral tools for graphs

T-Stochastic Graphs. Sijia Fang, Karl Rohe. arxiv version

Estimating Graph Dimension with Cross-validated Eigenvalues. Fan Chen, Sebastien Roch, Karl Rohe, Shuqi Yu. arxiv version. code

A New Basis for Sparse Principal Component Analysis. Fan Chen & Karl Rohe (2023). Journal of Computational and Graphical Statistics. journal version. arxiv version.

Vintage Factor Analysis with Varimax Performs Statistical Inference. Karl Rohe & Muzhe Zeng (2023). With discussion in JRSS-B. journal version. arXiv version. code.

Social Media Public Opinion as Flocks in a Murmuration: Conceptualizing and Measuring Opinion Expression on Social Media. Yini Zhang, Fan Chen, Karl Rohe. Journal of Computer-Mediated Communication. journal version

Targeted sampling from massive block model graphs with personalized pagerank. Fan Chen, Yini Zhang, Karl Rohe. JRSS-B, 2019. code

A note on quickly sampling a sparse matrix with low rank expectation. Karl Rohe, Jun Tao, Xintian Han, Norbert Binkiewicz. JMLR, 2018. code

Understanding regularized spectral clustering via graph conductance. Yilin Zhang, Karl Rohe. NeurIPS 2018

Discovering political topics in facebook discussion threads with graph contextualization. Yilin Zhang, Marie Poux-Berthe, Chris Wells, Karolina Koc-Michalska, Karl Rohe. Annals of Applied Statistics, 2018

Covariate-assisted spectral clustering. N. Binkiewicz, J.T. Vogelstein, K. Rohe. Biometrika, 2017.

Co-clustering directed graphs to discover asymmetries and directional communities. Karl Rohe, Tai Qin, Bin Yu. PNAS, 2016

Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel. Tai Qin, Karl Rohe. NeurIPS, 2013

Spectral clustering and the high dimensional stochastic blockmodel. K. Rohe, B. Yu, S. Chatterjee. Annals of Statistics, 2011

Attention and amplification in the hybrid media system: The composition and activity of Donald Trump's Twitter following during the 2016 presidential election. Y. Zhang, C. Wells, S. Wang, K. Rohe. New Media & Society, 2017

Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA. V. Vu, J. Cho, J. Lei, K. Rohe. NeurIPS, 2013.

Preconditioning the Lasso

Preconditioning to comply with the irrepresentable condition. Jinzhu Jia, Karl Rohe. Electronic Journal of Statistics, 2015.

Preconditioning for classical relationships: a note relating ridge regression and ols p-values to preconditioned sparse penalized regression. Karl Rohe. Stat, 2015.

Respondent-driven sampling

Network driven sampling; a critical threshold for design effects. Karl Rohe. Annals of Statistics, 2019

Generalized least squares can overcome the critical threshold in respondent-driven sampling. Sebastien Roch, Karl Rohe. PNAS, 2018

Novel sampling design for respondent-driven sampling. Mohammad Khabbazian, Bret Hanlon, Zoe Russek, Karl Rohe. Electronic Journal of Statistics, 2017

Central limit theorems for network driven sampling. Xiao Li, Karl Rohe. Electronic Journal of Statistics, 2017

Asymptotic seed bias in respondent-driven sampling. Yuling Yan, Bret Hanlon, Sebastien Roch, Karl Rohe. Electronic Journal of Statistics, 2020

Low-rank matrix completion

Intelligent initialization and adaptive thresholding for iterative matrix completion: Some statistical and algorithmic theory for adaptive-impute. Juhee Cho, Donggyu Kim, Karl Rohe. Journal of Computational and Graphical Statistics, 2019. code

Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise. Juhee Cho, Donggyu Kim, Karl Rohe. Statistica Sinica, 2017.

Latent factors in student–teacher interaction factor analysis. Thu Le, Daniel Bolt, Eric Camburn, Peter Goff, Karl Rohe. Journal of Educational and Behavioral Statistics, 2017.