Riemannian Zeroth-Order Gradient Estimation with Structure-Preserving Metrics for Geodesically Incomplete Manifolds
Fuente:
arXiv
Guardado en:
| Autores principales: | Ma, Shaocong, Huang, Heng |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
por: Ma, Shaocong, et al.
Publicado: (2025)
por: Ma, Shaocong, et al.
Publicado: (2025)
Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations
por: Ma, Shaocong, et al.
Publicado: (2025)
por: Ma, Shaocong, et al.
Publicado: (2025)
Obtaining Lower Query Complexities through Lightweight Zeroth-Order Proximal Gradient Algorithms
por: Gu, Bin, et al.
Publicado: (2024)
por: Gu, Bin, et al.
Publicado: (2024)
Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization
por: Chen, Ziyi, et al.
Publicado: (2025)
por: Chen, Ziyi, et al.
Publicado: (2025)
Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
por: Ma, Shaocong, et al.
Publicado: (2025)
por: Ma, Shaocong, et al.
Publicado: (2025)
On the Inherent Privacy of Zeroth Order Projected Gradient Descent
por: Gupta, Devansh, et al.
Publicado: (2025)
por: Gupta, Devansh, et al.
Publicado: (2025)
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
por: Chen, Jun, et al.
Publicado: (2023)
por: Chen, Jun, et al.
Publicado: (2023)
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
por: de Vazelhes, William, et al.
Publicado: (2022)
por: de Vazelhes, William, et al.
Publicado: (2022)
New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework
por: Ma, Shaocong, et al.
Publicado: (2026)
por: Ma, Shaocong, et al.
Publicado: (2026)
On Adaptivity in Zeroth-Order Optimization
por: Dbouk, Hassan, et al.
Publicado: (2026)
por: Dbouk, Hassan, et al.
Publicado: (2026)
VAMO: Efficient Zeroth-Order Variance Reduction for SGD with Faster Convergence
por: Chen, Jiahe, et al.
Publicado: (2025)
por: Chen, Jiahe, et al.
Publicado: (2025)
Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
por: Wang, Hongye, et al.
Publicado: (2025)
por: Wang, Hongye, et al.
Publicado: (2025)
Mirror Descent on Riemannian Manifolds
por: Jiang, Jiaxin, et al.
Publicado: (2026)
por: Jiang, Jiaxin, et al.
Publicado: (2026)
Zeroth-Order primal-dual Alternating Projection Gradient Algorithms for Nonconvex Minimax Problems with Coupled linear Constraints
por: Zhang, Huiling, et al.
Publicado: (2024)
por: Zhang, Huiling, et al.
Publicado: (2024)
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM
por: Refael, Yehonathan, et al.
Publicado: (2025)
por: Refael, Yehonathan, et al.
Publicado: (2025)
Fully Zeroth-Order Bilevel Programming via Gaussian Smoothing
por: Aghasi, Alireza, et al.
Publicado: (2024)
por: Aghasi, Alireza, et al.
Publicado: (2024)
A Framework for Bilevel Optimization on Riemannian Manifolds
por: Han, Andi, et al.
Publicado: (2024)
por: Han, Andi, et al.
Publicado: (2024)
A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning
por: Qiu, Yuyang, et al.
Publicado: (2025)
por: Qiu, Yuyang, et al.
Publicado: (2025)
Minimisation of Polyak-Łojasewicz Functions Using Random Zeroth-Order Oracles
por: Farzin, Amir Ali, et al.
Publicado: (2024)
por: Farzin, Amir Ali, et al.
Publicado: (2024)
Stochastic Zeroth order Descent with Structured Directions
por: Rando, Marco, et al.
Publicado: (2022)
por: Rando, Marco, et al.
Publicado: (2022)
Zeroth-Order Optimization at the Edge of Stability
por: Song, Minhak, et al.
Publicado: (2026)
por: Song, Minhak, et al.
Publicado: (2026)
Geometry-Preserving Neural Architectures on Manifolds with Boundary
por: Elamvazhuthi, Karthik, et al.
Publicado: (2026)
por: Elamvazhuthi, Karthik, et al.
Publicado: (2026)
Fast, Accurate Manifold Denoising by Tunneling Riemannian Optimization
por: Wang, Shiyu, et al.
Publicado: (2025)
por: Wang, Shiyu, et al.
Publicado: (2025)
Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization
por: Gu, Zhihao, et al.
Publicado: (2024)
por: Gu, Zhihao, et al.
Publicado: (2024)
Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator
por: Han, Haoyu, et al.
Publicado: (2026)
por: Han, Haoyu, et al.
Publicado: (2026)
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
por: Dodd, Daniel, et al.
Publicado: (2024)
por: Dodd, Daniel, et al.
Publicado: (2024)
Zeroth-Order Optimization Finds Flat Minima
por: Zhang, Liang, et al.
Publicado: (2025)
por: Zhang, Liang, et al.
Publicado: (2025)
Certified Multi-Fidelity Zeroth-Order Optimization
por: de Montbrun, Étienne, et al.
Publicado: (2023)
por: de Montbrun, Étienne, et al.
Publicado: (2023)
Private Zeroth-Order Nonsmooth Nonconvex Optimization
por: Zhang, Qinzi, et al.
Publicado: (2024)
por: Zhang, Qinzi, et al.
Publicado: (2024)
Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds
por: Sahinoglu, Emre, et al.
Publicado: (2025)
por: Sahinoglu, Emre, et al.
Publicado: (2025)
Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry
por: Zhang, Zhengxin, et al.
Publicado: (2024)
por: Zhang, Zhengxin, et al.
Publicado: (2024)
Riemannian Stochastic Gradient Method for Nested Composition Optimization
por: Zhang, Dewei, et al.
Publicado: (2022)
por: Zhang, Dewei, et al.
Publicado: (2022)
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function
por: Mhanna, Elissa, et al.
Publicado: (2024)
por: Mhanna, Elissa, et al.
Publicado: (2024)
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
por: Chen, Jun, et al.
Publicado: (2025)
por: Chen, Jun, et al.
Publicado: (2025)
Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
por: Yu, Qian, et al.
Publicado: (2024)
por: Yu, Qian, et al.
Publicado: (2024)
Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence
por: Jiao, Yang, et al.
Publicado: (2024)
por: Jiao, Yang, et al.
Publicado: (2024)
Minimisation of Submodular Functions Using Gaussian Zeroth-Order Random Oracles
por: Farzin, Amir Ali, et al.
Publicado: (2025)
por: Farzin, Amir Ali, et al.
Publicado: (2025)
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
por: Oowada, Kanata, et al.
Publicado: (2025)
por: Oowada, Kanata, et al.
Publicado: (2025)
Riemannian Dueling Optimization
por: Ren, Yuxuan, et al.
Publicado: (2026)
por: Ren, Yuxuan, et al.
Publicado: (2026)
Decentralized Online Riemannian Optimization Beyond Hadamard Manifolds
por: Sahinoglu, Emre, et al.
Publicado: (2025)
por: Sahinoglu, Emre, et al.
Publicado: (2025)
Ejemplares similares
-
On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
por: Ma, Shaocong, et al.
Publicado: (2025) -
Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations
por: Ma, Shaocong, et al.
Publicado: (2025) -
Obtaining Lower Query Complexities through Lightweight Zeroth-Order Proximal Gradient Algorithms
por: Gu, Bin, et al.
Publicado: (2024) -
Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization
por: Chen, Ziyi, et al.
Publicado: (2025) -
Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
por: Ma, Shaocong, et al.
Publicado: (2025)