Zachary Frangella

According to our database, Zachary Frangella authored at least 13 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project.

Pratik Rathore, Zachary Frangella, Sachin Garg, Shaghayegh Fazliani, Michal Derezinski, Madeleine Udell

CoRR, May, 2025

Enhancing Physics-Informed Neural Networks Through Feature Engineering.

Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell

CoRR, February, 2025

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning.

Jingruo Sun, Zachary Frangella, Madeleine Udell

CoRR, January, 2025

2024

SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates.

Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

SIAM J. Math. Data Sci., 2024

Have ASkotch: Fast Methods for Large-scale, Memory-constrained Kernel Ridge Regression.

Pratik Rathore, Zachary Frangella, Madeleine Udell

CoRR, 2024

CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks.

Miria Feng, Zachary Frangella, Mert Pilanci

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Challenges in Training PINNs: A Loss Landscape Perspective.

Pratik Rathore, Weimu Lei, Zachary Frangella, Lu Lu, Madeleine Udell

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Randomized Nyström Preconditioning.

Zachary Frangella, Joel A. Tropp, Madeleine Udell

SIAM J. Matrix Anal. Appl., June, 2023

PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates.

Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

CoRR, 2023

Robust, randomized preconditioning for kernel ridge regression.

Mateo Díaz, Ethan N. Epperly, Zachary Frangella, Joel A. Tropp, Robert J. Webber

CoRR, 2023

2022

SketchySGD: Reliable Stochastic Optimization via Robust Curvature Estimates.

Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell

CoRR, 2022

NysADMM: faster composite convex optimization via low-rank approximation.

Shipu Zhao, Zachary Frangella, Madeleine Udell

Proceedings of the International Conference on Machine Learning, 2022

2021

Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression.

William T. Stephenson, Zachary Frangella, Madeleine Udell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021