Randomized nyström preconditioning Z Frangella, JA Tropp, M Udell SIAM Journal on Matrix Analysis and Applications 44 (2), 718-752, 2023 | 41 | 2023 |
NysADMM: faster composite convex optimization via low-rank approximation S Zhao, Z Frangella, M Udell International Conference on Machine Learning, 26824-26840, 2022 | 11 | 2022 |
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression W Stephenson, Z Frangella, M Udell, T Broderick Advances in Neural Information Processing Systems 34, 24352-24364, 2021 | 10 | 2021 |
Challenges in training PINNs: A loss landscape perspective P Rathore, W Lei, Z Frangella, L Lu, M Udell arXiv preprint arXiv:2402.01868, 2024 | 9 | 2024 |
Robust, randomized preconditioning for kernel ridge regression M Díaz, EN Epperly, Z Frangella, JA Tropp, RJ Webber arXiv preprint arXiv:2304.12465, 2023 | 5 | 2023 |
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates Z Frangella, P Rathore, S Zhao, M Udell arXiv preprint arXiv:2211.08597, 2022 | 3 | 2022 |
On the (linear) convergence of Generalized Newton Inexact ADMM Z Frangella, S Zhao, T Diamandis, B Stellato, M Udell arXiv preprint arXiv:2302.03863, 2023 | 2 | 2023 |
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates Z Frangella, P Rathore, S Zhao, M Udell arXiv preprint arXiv:2309.02014, 2023 | 1 | 2023 |
Randomized Numerical Linear Algebra for Optimization M Udell, Z Frangella | | 2023 |
GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization T Diamandis, Z Frangella, S Zhao, B Stellato, M Udell arXiv preprint arXiv:2310.08333, 2023 | | 2023 |
Speeding up x= A\b with RandomizedPreconditioners. jl T Diamandis, Z Frangella | | 2022 |