Community Detection Using Spectral Clustering on Sparse Geosocial Data. - Yves van Gennip; Blake Hunter; Raymond Ahn; Peter Elliott; Kyle Luh; Megan Halvorson; Shannon Reid; Matthew Valasik; James Wo; George E. Tita; Andrea L. Bertozzi; P. Jeffrey Brantingham (SIAM J. Appl. Math., 2013)

Large-Deviation Bounds for Sampling without Replacement. - Kyle Luh; Nicholas Pippenger (Am. Math. Mon., 2014)

Random Matrices: l1 Concentration and Dictionary Learning with Few Samples. - Kyle Luh; Van Vu (FOCS, 2015)

Dictionary Learning With Few Samples and Matrix Concentration. - Kyle Luh; Van H. Vu (IEEE Trans. Inf. Theory, 2016)

Packing Loose Hamilton Cycles. - Asaf Ferber; Kyle Luh; Daniel Montealegre; Oanh Nguyen (Comb. Probab. Comput., 2017)

An Improved Lower Bound for Sparse Reconstruction from Subsampled Hadamard Matrices. - Jaroslaw Blasiok; Patrick Lopatto; Kyle Luh; Jake Marcinek; Shravas Rao (FOCS, 2019)

Four Deviations Suffice for Rank 1 Matrices. - Rasmus Kyng; Kyle Luh; Zhao Song 002 (CoRR, 2019)

Sparse Reconstruction from Hadamard Matrices: A Lower Bound. - Jaroslaw Blasiok; Patrick Lopatto; Kyle Luh; Jake Marcinek (CoRR, 2019)

On analog quantum algorithms for the mixing of Markov chains. - Shantanav Chakraborty; Kyle Luh; Jérémie Roland (CoRR, 2019)

A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates. - Zhixian Lei; Kyle Luh; Prayaag Venkat; Fred Zhang (COLT, 2020)

How fast do quantum walks mix? - Shantanav Chakraborty; Kyle Luh; Jérémie Roland (CoRR, 2020)

Eigenvector delocalization for non-Hermitian random matrices and applications. - Kyle Luh; Sean O'Rourke (Random Struct. Algorithms, 2020)

Resilience of the rank of random matrices. - Asaf Ferber; Kyle Luh; Gweneth McKinley (Comb. Probab. Comput., 2021)

A nonuniform Littlewood-Offord inequality for all norms. - Kyle Luh; David Xiang (Discret. Math., 2021)

Robustness Implies Generalization via Data-Dependent Generalization Bounds. - Kenji Kawaguchi; Zhun Deng; Kyle Luh; Jiaoyang Huang (ICML, 2022)