Publications
Ph.D. Thesis #
- Complexity, conditioning, and saddle avoidance in nonsmooth optimization
PhD Thesis, Cornell University, 2021. PDF
In the pipeline #
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Controlling the False Discovery Rate in Subspace Selection
(with V. Chandrasekaran) Submitted, 2024. PDF -
Robust, randomized preconditioning for kernel ridge regression
(with E. N. Epperly, Z. Frangella, J. A. Tropp, R. J. Webber) Submitted, 2023. PDF -
Clustering a Mixture of Gaussians with Unknown Covariance
(with D. Davis and K. Wang) Submitted, 2021. PDF
Published #
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Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality
(with J. Cutler and D. Drusvyatskiy) Journal of Machine Learning Research, 2024 (In press). PDF -
Any-dimensional equivariant neural networks
(with E. Levin) AISTATS, 2024. PDF -
Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming
(with D. Applegate, H. Lu, and M. Lubin) SIAM Journal on Optimization, 2024. PDF -
Optimal Convergence Rates for the Proximal Bundle Method
(with B. Grimmer) SIAM Journal on Optimization, 2023. PDF -
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
(with D. Davis and D. Drusvyatskiy) SIAM Journal on Optimization, 2022. PDF -
Optimization of vaccination for COVID-19 in the midst of a pandemic
(with Q. Luo, R. Weightman, S. T. McQuade, E. Trélat, W. Barbour, D. Work, S. Samanaranayake, B. Piccoli) Networks and Heterogeneous Media, 2022. PDF -
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
(with D. Applegate, O. Hinder, H. Lu, M. Lubin, B. O’Donoghue, and W. Schudy) NeurIPS, 2021. PDF -
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
(with V. Charisopoulos, Y. Chen, D. Davis, L. Ding, D. Drusvyatskiy) Foundations of Computational Mathematics, 2021. PDF -
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
(with K. Wang and Y. Yan) NeurIPS, 2020. PDF -
Composite optimization for robust rank one bilinear sensing
(with V. Charisopoulos, D. Davis, and D. Drusvyatskiy) Information and Inference, 2020. PDF -
Local angles and dimension estimation from data on manifolds
(with A. Quiroz, M. Velasco) Journal of Multivariate Analysis, 2019. PDF -
The nonsmooth landscape of blind deconvolution
Workshop on Optimization for Machine Learning, 2019. PDF -
Compressed sensing of data with a known distribution
(with M. Junca, F. Rincón and M. Velasco) Applied and Computational Harmonic Analysis, 2018. PDF -
In Search of Balance: The Challenge of Generating Balanced Latin Rectangles
(with C. Gomes, R. Le Bras) CPAIOR 2017. PDF