Zachary Frangella

According to our database1, Zachary Frangella authored at least 11 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2025
Enhancing Physics-Informed Neural Networks Through Feature Engineering.
CoRR, February, 2025

SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning.
CoRR, January, 2025

2024
Have ASkotch: Fast Methods for Large-scale, Memory-constrained Kernel Ridge Regression.
CoRR, 2024

CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks.
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.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Randomized Nyström Preconditioning.
SIAM J. Matrix Anal. Appl., June, 2023

PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates.
CoRR, 2023

Robust, randomized preconditioning for kernel ridge regression.
CoRR, 2023

2022
SketchySGD: Reliable Stochastic Optimization via Robust Curvature Estimates.
CoRR, 2022

NysADMM: faster composite convex optimization via low-rank approximation.
Proceedings of the International Conference on Machine Learning, 2022

2021
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021


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