PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
Fuente:
arXiv
Saved in:
| Main Authors: | Frangella, Zachary, Rathore, Pratik, Zhao, Shipu, Udell, Madeleine |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
by: Frangella, Zachary, et al.
Published: (2022)
by: Frangella, Zachary, et al.
Published: (2022)
Challenges in Training PINNs: A Loss Landscape Perspective
by: Rathore, Pratik, et al.
Published: (2024)
by: Rathore, Pratik, et al.
Published: (2024)
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
by: Rathore, Pratik, et al.
Published: (2025)
by: Rathore, Pratik, et al.
Published: (2025)
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
by: Rathore, Pratik, et al.
Published: (2024)
by: Rathore, Pratik, et al.
Published: (2024)
GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization
by: Diamandis, Theo, et al.
Published: (2023)
by: Diamandis, Theo, et al.
Published: (2023)
An automatic system to detect equivalence between iterative algorithms
by: Zhao, Shipu, et al.
Published: (2021)
by: Zhao, Shipu, et al.
Published: (2021)
SAPPHIRE: Preconditioned Stochastic Variance Reduction for Faster Large-Scale Statistical Learning
by: Sun, Jingruo, et al.
Published: (2025)
by: Sun, Jingruo, et al.
Published: (2025)
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
by: Feng, Miria, et al.
Published: (2024)
by: Feng, Miria, et al.
Published: (2024)
Small Gradient Norm Regret for Online Convex Optimization
by: Gao, Wenzhi, et al.
Published: (2026)
by: Gao, Wenzhi, et al.
Published: (2026)
On the (linear) convergence of Generalized Newton Inexact ADMM
by: Frangella, Zachary, et al.
Published: (2023)
by: Frangella, Zachary, et al.
Published: (2023)
Gradient Methods with Online Scaling
by: Gao, Wenzhi, et al.
Published: (2024)
by: Gao, Wenzhi, et al.
Published: (2024)
Gradient Methods with Online Scaling Part I. Theoretical Foundations
by: Gao, Wenzhi, et al.
Published: (2025)
by: Gao, Wenzhi, et al.
Published: (2025)
Gradient Methods with Online Scaling Part II. Practical Aspects
by: Chu, Ya-Chi, et al.
Published: (2025)
by: Chu, Ya-Chi, et al.
Published: (2025)
Scalable Approximate Optimal Diagonal Preconditioning
by: Gao, Wenzhi, et al.
Published: (2023)
by: Gao, Wenzhi, et al.
Published: (2023)
Stochastic Gradient Methods with Preconditioned Updates
by: Sadiev, Abdurakhmon, et al.
Published: (2022)
by: Sadiev, Abdurakhmon, et al.
Published: (2022)
Provable and Practical Online Learning Rate Adaptation with Hypergradient Descent
by: Chu, Ya-Chi, et al.
Published: (2025)
by: Chu, Ya-Chi, et al.
Published: (2025)
Adaptive Step Sizes for Preconditioned Stochastic Gradient Descent
by: Köhne, Frederik, et al.
Published: (2023)
by: Köhne, Frederik, et al.
Published: (2023)
Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
by: Oikonomidis, Konstantinos, et al.
Published: (2026)
by: Oikonomidis, Konstantinos, et al.
Published: (2026)
Understanding Fixed Predictions via Confined Regions
by: Lawless, Connor, et al.
Published: (2025)
by: Lawless, Connor, et al.
Published: (2025)
Enhancing Physics-Informed Neural Networks Through Feature Engineering
by: Fazliani, Shaghayegh, et al.
Published: (2025)
by: Fazliani, Shaghayegh, et al.
Published: (2025)
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning
by: Zhang, Thomas T., et al.
Published: (2025)
by: Zhang, Thomas T., et al.
Published: (2025)
Memory-Efficient 4-bit Preconditioned Stochastic Optimization
by: Li, Jingyang, et al.
Published: (2024)
by: Li, Jingyang, et al.
Published: (2024)
When Does Primal Interior Point Method Beat Primal-dual in Linear Optimization?
by: Gao, Wenzhi, et al.
Published: (2024)
by: Gao, Wenzhi, et al.
Published: (2024)
Low-Rank Extragradient Methods for Scalable Semidefinite Optimization
by: Garber, Dan, et al.
Published: (2024)
by: Garber, Dan, et al.
Published: (2024)
A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training
by: Angino, Andrea, et al.
Published: (2026)
by: Angino, Andrea, et al.
Published: (2026)
Riemannian Stochastic Gradient Method for Nested Composition Optimization
by: Zhang, Dewei, et al.
Published: (2022)
by: Zhang, Dewei, et al.
Published: (2022)
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
by: Kwon, Jeongyeol, et al.
Published: (2024)
by: Kwon, Jeongyeol, et al.
Published: (2024)
Stochastic Approximation Methods for Distortion Risk Measure Optimization
by: Jiang, Jinyang, et al.
Published: (2025)
by: Jiang, Jinyang, et al.
Published: (2025)
Efficient Low-Tubal-Rank Tensor Estimation via Alternating Preconditioned Gradient Descent
by: Liu, Zhiyu, et al.
Published: (2025)
by: Liu, Zhiyu, et al.
Published: (2025)
Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
by: Hu, Yifan, et al.
Published: (2020)
by: Hu, Yifan, et al.
Published: (2020)
Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
by: Veprikov, Andrey, et al.
Published: (2025)
by: Veprikov, Andrey, et al.
Published: (2025)
Algebraic characterization of equivalence between oracle-based iterative algorithms
by: Lessard, Laurent, et al.
Published: (2025)
by: Lessard, Laurent, et al.
Published: (2025)
Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization
by: Chen, Lesi, et al.
Published: (2025)
by: Chen, Lesi, et al.
Published: (2025)
Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization
by: Chen, Ziyi, et al.
Published: (2025)
by: Chen, Ziyi, et al.
Published: (2025)
Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
by: Liu, Wei, et al.
Published: (2025)
by: Liu, Wei, et al.
Published: (2025)
Riemannian Optimization for Hadamard Products of Low-Rank Matrices
by: Jawanpuria, Pratik, et al.
Published: (2026)
by: Jawanpuria, Pratik, et al.
Published: (2026)
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation
by: Kwon, Jeongyeol, et al.
Published: (2023)
by: Kwon, Jeongyeol, et al.
Published: (2023)
Tree-Preconditioned Differentiable Optimization and Axioms as Layers
by: Liao, Yuexin
Published: (2025)
by: Liao, Yuexin
Published: (2025)
PolarGrad: A Class of Matrix-Gradient Optimizers from a Unifying Preconditioning Perspective
by: Lau, Tim Tsz-Kit, et al.
Published: (2025)
by: Lau, Tim Tsz-Kit, et al.
Published: (2025)
Efficient Curvature-Aware Hypergradient Approximation for Bilevel Optimization
by: Dong, Youran, et al.
Published: (2025)
by: Dong, Youran, et al.
Published: (2025)
Similar Items
-
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
by: Frangella, Zachary, et al.
Published: (2022) -
Challenges in Training PINNs: A Loss Landscape Perspective
by: Rathore, Pratik, et al.
Published: (2024) -
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
by: Rathore, Pratik, et al.
Published: (2025) -
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
by: Rathore, Pratik, et al.
Published: (2024) -
GeNIOS: an (almost) second-order operator-splitting solver for large-scale convex optimization
by: Diamandis, Theo, et al.
Published: (2023)