Zachary Frangella

20 papers A* 4Journal 16

Year	Rank	Type	Title / 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