Low-rank matrix completion and denoising under Poisson noise. - Andrew D. McRae; Mark A. Davenport (CoRR, 2019)

Sample complexity and effective dimension for regression on manifolds. - Andrew D. McRae; Justin Romberg; Mark A. Davenport (NeurIPS, 2020)

Harmless interpolation in regression and classification with structured features. - Andrew D. McRae; Santhosh Karnik; Mark A. Davenport; Vidya K. Muthukumar (AISTATS, 2022)

Delta Distancing: A Lifting Approach to Localizing Items from User Comparisons. - Andrew D. McRae; Austin Xu; Jihui Jin; Namrata Nadagouda; Nauman Ahad; Peimeng Guan; Santhosh Karnik; Mark A. Davenport (ICASSP, 2022)

Structured Statistical Estimation via Optimization. - Andrew D. McRae (2022)

Perceptual adjustment queries and an inverted measurement paradigm for low-rank metric learning. - Austin Xu; Andrew D. McRae; Jingyan Wang 001; Mark A. Davenport; Ashwin Pananjady (NeurIPS, 2023)

Optimal Convex Lifted Sparse Phase Retrieval and PCA With an Atomic Matrix Norm Regularizer. - Andrew D. McRae; Justin Romberg; Mark A. Davenport (IEEE Trans. Inf. Theory, 2023)

Synchronization on circles and spheres with nonlinear interactions. - Christopher Criscitiello; Quentin Rebjock; Andrew D. McRae; Nicolas Boumal (CoRR, 2024)

Benign Landscapes of Low-Dimensional Relaxations for Orthogonal Synchronization on General Graphs. - Andrew D. McRae; Nicolas Boumal (SIAM J. Optim., 2024)

New Equivalences between Interpolation and SVMs: Kernels and Structured Features. - Chiraag Kaushik; Andrew D. McRae; Mark A. Davenport; Vidya Muthukumar (SIAM J. Math. Data Sci., 2024)

Sensor network localization has a benign landscape after low-dimensional relaxation. - Christopher Criscitiello; Andrew D. McRae; Quentin Rebjock; Nicolas Boumal (CoRR, 2025)