Zachary Frangella

20 papers A* 4Journal 16
YearRankTypeTitle / Venue / Authors
2026 J jnl
On the (linear) convergence of Generalized Newton Inexact ADMM.
Trans. Mach. Learn. Res.
Zachary Frangella, Theo Diamandis, Bartolomeo Stellato, Madeleine Udell
2025 J jnl
Enhancing Physics-Informed Neural Networks Through Feature Engineering.
CoRR
Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell
2025 J jnl
Enhancing Physics-Informed Neural Networks Through Feature Engineering.
Trans. Mach. Learn. Res.
Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell
2025 J jnl
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning.
CoRR
Jingruo Sun, Zachary Frangella, Madeleine Udell
2025 J jnl
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project.
CoRR
Pratik Rathore, Zachary Frangella, Sachin Garg, Shaghayegh Fazliani, Michal Derezinski, Madeleine Udell
2024 A* conf
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks.
NeurIPS
Miria Feng, Zachary Frangella, Mert Pilanci
2024 J jnl
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks.
CoRR
Miria Feng, Zachary Frangella, Mert Pilanci
2024 A* conf
Challenges in Training PINNs: A Loss Landscape Perspective.
ICML
Pratik Rathore, Weimu Lei, Zachary Frangella, Lu Lu, Madeleine Udell
2024 J jnl
Challenges in Training PINNs: A Loss Landscape Perspective.
CoRR
Pratik Rathore, Weimu Lei, Zachary Frangella, Lu Lu, Madeleine Udell
2024 J jnl
Have ASkotch: Fast Methods for Large-scale, Memory-constrained Kernel Ridge Regression.
CoRR
Pratik Rathore, Zachary Frangella, Madeleine Udell
2024 J jnl
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates.
J. Mach. Learn. Res.
Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell
2024 J jnl
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates.
SIAM J. Math. Data Sci.
Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell
2023 J jnl
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates.
CoRR
Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell
2023 J jnl
Randomized Nyström Preconditioning.
SIAM J. Matrix Anal. Appl.
Zachary Frangella, Joel A. Tropp, Madeleine Udell
2023 J jnl
Robust, randomized preconditioning for kernel ridge regression.
CoRR
Mateo Díaz, Ethan N. Epperly, Zachary Frangella, Joel A. Tropp, Robert J. Webber
2022 A* conf
NysADMM: faster composite convex optimization via low-rank approximation.
ICML
Shipu Zhao, Zachary Frangella, Madeleine Udell
2022 J jnl
SketchySGD: Reliable Stochastic Optimization via Robust Curvature Estimates.
CoRR
Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell
2021 A* conf
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression.
NeurIPS
William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick
2021 J jnl
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression.
CoRR
William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick
2021 J jnl
Randomized Nyström Preconditioning.
CoRR
Zachary Frangella, Joel A. Tropp, Madeleine Udell