Imagen de Portada

Zeroth-order Low-rank Hessian Estimation via Matrix Recovery

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wang, Tianyu, Wang, Zicheng, Yu, Jiajia
Formato:	Preprint
Publicado:	2024
Materias:	Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Zeroth-order Stochastic Cubic Newton Method Revisited
por: Liu, Yu, et al.
Publicado: (2024)

Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?
por: Ma, Jianhao, et al.
Publicado: (2023)

Nonnegative Low-rank Matrix Recovery Can Have Spurious Local Minima
por: Zhang, Richard Y.
Publicado: (2025)

An Inexact Preconditioned Zeroth-order Proximal Method for Composite Optimization
por: Liu, Shanglin, et al.
Publicado: (2024)

Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
por: Giampouras, Paris, et al.
Publicado: (2024)

Efficient Low Rank Matrix Recovery With Flexible Group Sparse Regularization
por: Yu, Quan, et al.
Publicado: (2024)

Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
por: Yu, Qian, et al.
Publicado: (2024)

Riemannian Accelerated Zeroth-order Algorithm: Improved Robustness and Lower Query Complexity
por: He, Chang, et al.
Publicado: (2024)

Minimization of Nonsmooth Weakly Convex Function over Prox-regular Set for Robust Low-rank Matrix Recovery
por: Kume, Keita, et al.
Publicado: (2025)

Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization
por: Shi, Qiankun, et al.
Publicado: (2025)

Complexity Guarantees for Zeroth-order Methods via Exponentially-shifted Gaussian Smoothing: Mitigating Dimension-dependence and Incorporating Decision-dependence
por: Wang, Mingrui, et al.
Publicado: (2026)

Accelerated Gradient Methods via Inertial Systems with Hessian-driven Damping
por: Wang, Zepeng, et al.
Publicado: (2025)

Stochastic Zeroth order Descent with Structured Directions
por: Rando, Marco, et al.
Publicado: (2022)

Zeroth-order Gradient and Quasi-Newton Methods for Nonsmooth Nonconvex Stochastic Optimization
por: Marrinan, Luke, et al.
Publicado: (2023)

Robust Low-rank Tensor Train Recovery
por: Qin, Zhen, et al.
Publicado: (2024)

First- and Zeroth-Order Learning in Asynchronous Games
por: Wang, Zifan, et al.
Publicado: (2025)

A Zeroth-order Proximal Stochastic Gradient Method for Weakly Convex Stochastic Optimization
por: Pougkakiotis, Spyridon, et al.
Publicado: (2022)

Compressed Zeroth-Order Algorithm for Stochastic Distributed Nonconvex Optimization
por: Wang, Haonan, et al.
Publicado: (2025)

Projected Gradient Descent Algorithm for Low-Rank Matrix Estimation
por: Zhang, Teng, et al.
Publicado: (2024)

Fast convex optimization via inertial systems with asymptotically vanishing viscosity and Hessian-driven damping
por: Wang, Zepeng, et al.
Publicado: (2025)

A Zeroth-order Resilient Algorithm for Distributed Online Optimization against Byzantine Edge Attacks
por: Liu, Yuhang, et al.
Publicado: (2025)

In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
por: Philippenko, Constantin, et al.
Publicado: (2024)

Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime
por: Zhang, Richard Y.
Publicado: (2021)

Heterogeneous Distributed Zeroth-Order Nonconvex Optimization with Communication Compression
por: Wang, Haonan, et al.
Publicado: (2026)

Efficient Low-rank Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization
por: Wang, Hao, et al.
Publicado: (2024)

Revisit First-order Methods for Geodesically Convex Optimization
por: Shu, Yunlu, et al.
Publicado: (2025)

Variance-Reduced Gradient Estimator for Nonconvex Zeroth-Order Distributed Optimization
por: Mu, Huaiyi, et al.
Publicado: (2024)

Can a One-Point Feedback Zeroth-order Algorithm Achieve Linear Dimension Dependent Sample Complexity?
por: Ye, Haishan, et al.
Publicado: (2025)

Optimal Zeroth-Order Bilevel Optimization
por: Aghasi, Alireza, et al.
Publicado: (2025)

Skip the Hessian, Keep the Rates: Globalized Semismooth Newton with Lazy Hessian Updates
por: Alphonse, Amal, et al.
Publicado: (2026)

Zeroth-Order Non-smooth Non-convex Optimization via Gaussian Smoothing
por: Paul, Anik Kumar, et al.
Publicado: (2025)

A Unified Zeroth-Order Optimization Framework via Oblivious Randomized Sketching
por: Ye, Haishan, et al.
Publicado: (2025)

A Zeroth-Order Extra-Gradient Method for Black-Box Constrained Optimization
por: Zhou, Yuke, et al.
Publicado: (2025)

Eliminating Persistent Boundary Residence via Matrosov-Type Auxiliary Functions
por: Han, Tianyu, et al.
Publicado: (2026)

Low-Order Explicit Hessian Imitation Method for Large-Scale Supervised Machine Learning
por: Zhu, Yunlang, et al.
Publicado: (2026)

Complexity of Zeroth- and First-order Stochastic Trust-Region Algorithms
por: Ha, Yunsoo, et al.
Publicado: (2024)

Zeroth-Order Constrained Optimization from a Control Perspective via Feedback Linearization
por: Zhang, Runyu, et al.
Publicado: (2025)

Nonsmooth Optimization with Zeroth Order Comparison Feedback
por: Bakkali, Taha El, et al.
Publicado: (2026)

Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent
por: Liu, Zhiyu, et al.
Publicado: (2024)

ZORMS-LfD: Learning from Demonstrations with Zeroth-Order Random Matrix Search
por: Dry, Olivia, et al.
Publicado: (2025)