Approximation rates for interpolation of Sobolev functions via Gaussians and allied functions. - Keaton Hamm (J. Approx. Theory, 2015)

Cardinal interpolation with general multiquadrics. - Keaton Hamm; Jeff Ledford (Adv. Comput. Math., 2016)

Principal coordinate clustering. - Ali Sekmen; Akram Aldroubi; Ahmet Bugra Koku; Keaton Hamm (IEEE BigData, 2017)

Matrix resconstruction: Skeleton decomposition versus singular value decomposition. - Ali Sekmen; Akram Aldroubi; Ahmet Bugra Koku; Keaton Hamm (SPECTS, 2017)

Cardinal interpolation with general multiquadrics: convergence rates. - Keaton Hamm; Jeff Ledford (Adv. Comput. Math., 2018)

CUR Decompositions, Similarity Matrices, and Subspace Clustering. - Akram Aldroubi; Keaton Hamm; Ahmet Bugra Koku; Ali Sekmen (Frontiers Appl. Math. Stat., 2018)

Approximation Algorithms and an Integer Program for Multi-level Graph Spanners. - Abu Reyan Ahmed; Keaton Hamm; Mohammad Javad Latifi Jebelli; Stephen G. Kobourov; Faryad Darabi Sahneh; Richard Spence (SEA², 2019)

CUR Decompositions, Approximations, and Perturbations. - Keaton Hamm; Longxiu Huang (CoRR, 2019)

A General Framework for Multi-level Subsetwise Graph Sparsifiers. - Abu Reyan Ahmed; Keaton Hamm; Mohammad Javad Latifi Jebelli; Stephen G. Kobourov; Faryad Darabi Sahneh; Richard Spence (CoRR, 2019)

Perspectives on CUR Decompositions. - Keaton Hamm; Longxiu Huang (CoRR, 2019)

Kruskal-Based Approximation Algorithm for the Multi-Level Steiner Tree Problem. - Abu Reyan Ahmed; Faryad Darabi Sahneh; Keaton Hamm; Stephen G. Kobourov; Richard Spence (ESA, 2020)

Stability of Sampling for CUR Decompositions. - Keaton Hamm; Longxiu Huang (CoRR, 2020)

Graph spanners: A tutorial review. - Abu Reyan Ahmed; Greg Bodwin; Faryad Darabi Sahneh; Keaton Hamm; Mohammad Javad Latifi Jebelli; Stephen G. Kobourov; Richard Spence (Comput. Sci. Rev., 2020)

Multi-Level Weighted Additive Spanners. - Abu Reyan Ahmed; Greg Bodwin; Faryad Darabi Sahneh; Keaton Hamm; Stephen G. Kobourov; Richard Spence (SEA, 2021)

On Additive Spanners in Weighted Graphs with Local Error. - Abu Reyan Ahmed; Greg Bodwin; Keaton Hamm; Stephen G. Kobourov; Richard Spence (WG, 2021)

Weighted Sparse and Lightweight Spanners with Local Additive Error. - Abu Reyan Ahmed; Greg Bodwin; Keaton Hamm; Stephen G. Kobourov; Richard Spence (CoRR, 2021)

On Matrix Factorizations in Subspace Clustering. - Reeshad Arian; Keaton Hamm (CoRR, 2021)

Computing Steiner Trees using Graph Neural Networks. - Abu Reyan Ahmed; Md Asadullah Turja; Faryad Darabi Sahneh; Mithun Ghosh; Keaton Hamm; Stephen G. Kobourov (CoRR, 2021)

Editorial: Mathematical Fundamentals of Machine Learning. - David Glickenstein; Keaton Hamm; Xiaoming Huo; Yajun Mei; Martin Stoll (Frontiers Appl. Math. Stat., 2021)

Mode-wise Tensor Decompositions: Multi-dimensional Generalizations of CUR Decompositions. - HanQin Cai; Keaton Hamm; Longxiu Huang; Deanna Needell (J. Mach. Learn. Res., 2021)

Robust CUR Decomposition: Theory and Imaging Applications. - HanQin Cai; Keaton Hamm; Longxiu Huang; Deanna Needell (SIAM J. Imaging Sci., 2021)

Perturbations of CUR Decompositions. - Keaton Hamm; Longxiu Huang (SIAM J. Matrix Anal. Appl., 2021)

Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation. - HanQin Cai; Keaton Hamm; Longxiu Huang; Jiaqi Li; Tao Wang (IEEE Signal Process. Lett., 2021)

Riemannian CUR Decompositions for Robust Principal Component Analysis. - Keaton Hamm; Mohamed Meskini; HanQin Cai (TAG-ML, 2022)

Generalized Pseudoskeleton Decompositions. - Keaton Hamm (CoRR, 2022)

Multi-priority Graph Sparsification. - Abu Reyan Ahmed; Keaton Hamm; Stephen G. Kobourov; Mohammad Javad Latifi Jebelli; Faryad Darabi Sahneh; Richard Spence (IWOCA, 2023)

Linearized Wasserstein dimensionality reduction with approximation guarantees. - Alexander Cloninger; Keaton Hamm; Varun Khurana; Caroline Moosmüller (CoRR, 2023)

Boosting Nyström Method. - Keaton Hamm; Zhaoying Lu; Wenbo Ouyang; Hao Helen Zhang (CoRR, 2023)

On Wasserstein distances for affine transformations of random vectors. - Keaton Hamm; Andrzej Korzeniowski (CoRR, 2023)

Lattice Approximations in Wasserstein Space. - Keaton Hamm; Varun Khurana (CoRR, 2023)

Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning. - Keaton Hamm; Nick Henscheid; Shujie Kang (SIAM J. Math. Data Sci., 2023)

Persistent Classification: A New Approach to Stability of Data and Adversarial Examples. - Brian Bell 002; Michael Geyer; David Glickenstein; Keaton Hamm; Carlos Scheidegger; Amanda S. Fernandez; Juston Moore (CoRR, 2024)

Manifold Learning in Wasserstein Space. - Keaton Hamm; Caroline Moosmüller; Bernhard Schmitzer; Matthew Thorpe (SIAM J. Math. Anal., 2025)

Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics. - Brian Bell 002; Michael Geyer; David Glickenstein; Keaton Hamm; Carlos Scheidegger; Amanda S. Fernandez; Juston Moore (Stat. Anal. Data Min., 2025)