In Search of Balance: The Challenge of Generating Balanced Latin Rectangles. - Mateo Díaz; Ronan Le Bras 001; Carla P. Gomes (CPAIOR, 2017)

Composite optimization for robust blind deconvolution. - Vasileios Charisopoulos; Damek Davis; Mateo Díaz; Dmitriy Drusvyatskiy (CoRR, 2019)

Local angles and dimension estimation from data on manifolds. - Mateo Díaz; Adolfo J. Quiroz; Mauricio Velasco (J. Multivar. Anal., 2019)

Efficient Clustering for Stretched Mixtures: Landscape and Optimality. - Kaizheng Wang; Yuling Yan; Mateo Díaz (NeurIPS, 2020)

Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient. - David L. Applegate; Mateo Díaz; Oliver Hinder; Haihao Lu; Miles Lubin; Brendan O'Donoghue; Warren Schudy (NeurIPS, 2021)

Clustering a Mixture of Gaussians with Unknown Covariance. - Damek Davis; Mateo Díaz; Kaizheng Wang (CoRR, 2021)

Low-Rank Matrix Recovery with Composite Optimization: Good Conditioning and Rapid Convergence. - Vasileios Charisopoulos; Yudong Chen 001; Damek Davis; Mateo Díaz; Lijun Ding; Dmitriy Drusvyatskiy (Found. Comput. Math., 2021)

Optimization of vaccination for COVID-19 in the midst of a pandemic. - Qi Luo; Ryan Weightman; Sean T. McQuade; Mateo Díaz; Emmanuel Trélat; William Barbour; Dan Work; Samitha Samaranayake; Benedetto Piccoli (Networks Heterog. Media, 2022)

Escaping Strict Saddle Points of the Moreau Envelope in Nonsmooth Optimization. - Damek Davis; Mateo Díaz; Dmitriy Drusvyatskiy (SIAM J. Optim., 2022)

Robust, randomized preconditioning for kernel ridge regression. - Mateo Díaz; Ethan N. Epperly; Zachary Frangella; Joel A. Tropp; Robert J. Webber (CoRR, 2023)

Optimal Convergence Rates for the Proximal Bundle Method. - Mateo Díaz; Benjamin Grimmer (SIAM J. Optim., 2023)

Any-dimensional equivariant neural networks. - Eitan Levin; Mateo Díaz (AISTATS, 2024)

Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality. - Joshua Cutler; Mateo Díaz; Dmitriy Drusvyatskiy (J. Mach. Learn. Res., 2024)

Infeasibility Detection with Primal-Dual Hybrid Gradient for Large-Scale Linear Programming. - David L. Applegate; Mateo Díaz; Haihao Lu; Miles Lubin (SIAM J. Optim., 2024)

On Transferring Transferability: Towards a Theory for Size Generalization. - Eitan Levin; Yuxin Ma; Mateo Díaz; Soledad Villar (CoRR, 2025)

Beyond Scores: Proximal Diffusion Models. - Zhenghan Fang; Mateo Díaz; Sam Buchanan; Jeremias Sulam (CoRR, 2025)