Chronos: A Tool for Interactive Scheduling and Visualisation of Task Hierarchies. - Saturnino Luz; Masood Masoodian; Daniel McKenzie; Wim Vanden Broeck (IV, 2009)

A Compressive Sensing Approach to Community Detection with Applications. - Ming-Jun Lai; Daniel McKenzie (CoRR, 2017)

Semi-Supervised Cluster Extraction via a Compressive Sensing Approach. - Ming-Jun Lai; Daniel McKenzie (CoRR, 2018)

Power Weighted Shortest Paths for Unsupervised Learning. - Daniel McKenzie; Steven Damelin (CoRR, 2019)

Who killed Lilly Kane? A case study in applying knowledge graphs to crime fiction. - Mariam Alaverdian; William Gilroy; Veronica Kirgios; Xia Li; Carolina Matuk; Daniel McKenzie; Tachin Ruangkriengsin; Andrea L. Bertozzi; P. Jeffrey Brantingham (IEEE BigData, 2020)

SCOBO: Sparsity-Aware Comparison Oracle Based Optimization. - HanQin Cai; Daniel McKenzie; Wotao Yin; Zhenliang Zhang 001 (CoRR, 2020)

Compressive Sensing for Cut Improvement and Local Clustering. - Ming-Jun Lai; Daniel McKenzie (SIAM J. Math. Data Sci., 2020)

A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization. - HanQin Cai; Yuchen Lou; Daniel McKenzie; Wotao Yin (ICML, 2021)

Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop. - Samy Wu Fung; Howard Heaton; Qiuwei Li; Daniel McKenzie; Stanley J. Osher; Wotao Yin (CoRR, 2021)

Learn to Predict Equilibria via Fixed Point Networks. - Howard Heaton; Daniel McKenzie; Qiuwei Li; Samy Wu Fung; Stanley J. Osher; Wotao Yin (CoRR, 2021)

JFB: Jacobian-Free Backpropagation for Implicit Networks. - Samy Wu Fung; Howard Heaton; Qiuwei Li; Daniel McKenzie; Stanley J. Osher; Wotao Yin (AAAI, 2022)

Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling. - HanQin Cai; Daniel McKenzie; Wotao Yin; Zhenliang Zhang 001 (SIAM J. Optim., 2022)

Balancing Geometry and Density: Path Distances on High-Dimensional Data. - Anna V. Little; Daniel McKenzie; James M. Murphy (SIAM J. Math. Data Sci., 2022)

Faster Predict-and-Optimize with Three-Operator Splitting. - Daniel McKenzie; Samy Wu Fung; Howard Heaton (CoRR, 2023)

It begins with a boundary: A geometric view on probabilistically robust learning. - Leon Bungert; Nicolás García Trillos; Matt Jacobs; Daniel McKenzie; Dorde Nikolic; Qingsong Wang (CoRR, 2023)

Normalizing Basis Functions: Approximate Stationary Models for Large Spatial Data. - Antony Sikorski; Daniel McKenzie; Douglas Nychka (CoRR, 2024)

On Logical Extrapolation for Mazes with Recurrent and Implicit Networks. - Brandon Knutson; Amandin Chyba Rabeendran; Michael I. Ivanitskiy; Jordan Pettyjohn; Cecilia G. Diniz Behn; Samy Wu Fung; Daniel McKenzie (CoRR, 2024)

Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms. - Nicolás García Trillos; Anna V. Little; Daniel McKenzie; James M. Murphy (J. Mach. Learn. Res., 2024)

Three-Operator Splitting for Learning to Predict Equilibria in Convex Games. - Daniel McKenzie; Howard Heaton; Qiuwei Li; Samy Wu Fung; Stanley J. Osher; Wotao Yin (SIAM J. Math. Data Sci., 2024)

Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin Splitting. - Daniel McKenzie; Howard Heaton; Samy Wu Fung (Trans. Mach. Learn. Res., 2024)

LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data. - Antony Sikorski; Michael I. Ivanitskiy; Nathan Lenssen; Douglas Nychka; Daniel McKenzie (CoRR, 2025)

Curvature-Aware Derivative-Free Optimization. - Bumsu Kim; Daniel McKenzie; HanQin Cai; Wotao Yin (J. Sci. Comput., 2025)