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Zachary Frangella
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Year
Randomized nyström preconditioning
Z Frangella, JA Tropp, M Udell
SIAM Journal on Matrix Analysis and Applications 44 (2), 718-752, 2023
392023
NysADMM: faster composite convex optimization via low-rank approximation
S Zhao, Z Frangella, M Udell
International Conference on Machine Learning, 26824-26840, 2022
102022
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
82021
Robust, randomized preconditioning for kernel ridge regression
M Díaz, EN Epperly, Z Frangella, JA Tropp, RJ Webber
arXiv preprint arXiv:2304.12465, 2023
32023
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
Z Frangella, P Rathore, S Zhao, M Udell
arXiv preprint arXiv:2211.08597, 2022
32022
Challenges in training PINNs: A loss landscape perspective
P Rathore, W Lei, Z Frangella, L Lu, M Udell
arXiv preprint arXiv:2402.01868, 2024
22024
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
22023
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
Z Frangella, P Rathore, S Zhao, M Udell
arXiv preprint arXiv:2309.02014, 2023
12023
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
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