Copertina

StelLA: Subspace Learning in Low-rank Adaptation using Stiefel Manifold

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Zhizhong, Sajadmanesh, Sina, Li, Jingtao, Lyu, Lingjuan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Replay-Free Continual Low-Rank Adaptation with Dynamic Memory
di: Chen, Huancheng, et al.
Pubblicazione: (2024)

Empirical Recipes for Efficient and Compact Vision-Language Models
di: Huang, Jiabo, et al.
Pubblicazione: (2026)

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
di: Sun, Guangyu, et al.
Pubblicazione: (2025)

Semi-Supervised Laplace Learning on Stiefel Manifolds
di: Holtz, Chester, et al.
Pubblicazione: (2023)

Calibrated Adaptation: Bayesian Stiefel Manifold Priors for Reliable Parameter-Efficient Fine-Tuning
di: Shihab, Ibne Farabi, et al.
Pubblicazione: (2026)

FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free
di: Yuan, Haolin, et al.
Pubblicazione: (2025)

Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget
di: Sehwag, Vikash, et al.
Pubblicazione: (2024)

When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
di: Zhuang, Weiming, et al.
Pubblicazione: (2023)

ExpVG: Investigating the Design Space of Visual Grounding in Multimodal Large Language Model
di: Kang, Weitai, et al.
Pubblicazione: (2025)

Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations
di: Xie, Zhihui, et al.
Pubblicazione: (2024)

Riemannian Optimization for LoRA on the Stiefel Manifold
di: Park, Juneyoung, et al.
Pubblicazione: (2025)

UNIFORM: Unifying Knowledge from Large-scale and Diverse Pre-trained Models
di: Wang, Yimu, et al.
Pubblicazione: (2025)

FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
di: Wang, Ziyao, et al.
Pubblicazione: (2024)

Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping
di: Fan, Ziqing, et al.
Pubblicazione: (2024)

Mixture-of-Subspaces in Low-Rank Adaptation
di: Wu, Taiqiang, et al.
Pubblicazione: (2024)

SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in LLMs
di: Jayasuriya, Dinithi, et al.
Pubblicazione: (2025)

Don't be so Stief! Learning KV Cache low-rank approximation over the Stiefel manifold
di: Benfenati, Luca, et al.
Pubblicazione: (2026)

Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
di: Chen, Jun, et al.
Pubblicazione: (2023)

Optimized Weight Initialization on the Stiefel Manifold for Deep ReLU Neural Networks
di: Lee, Hyungu, et al.
Pubblicazione: (2025)

FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds
di: Tabealhojeh, Hadi, et al.
Pubblicazione: (2024)

VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations
di: Patel, Maitreya, et al.
Pubblicazione: (2026)

FedWon: Triumphing Multi-domain Federated Learning Without Normalization
di: Zhuang, Weiming, et al.
Pubblicazione: (2023)

Bayesian Low-rank Adaptation for Large Language Models
di: Yang, Adam X., et al.
Pubblicazione: (2023)

Robustness Beyond Known Groups with Low-rank Adaptation
di: Gourabathina, Abinitha, et al.
Pubblicazione: (2026)

Sybil-based Virtual Data Poisoning Attacks in Federated Learning
di: Zhu, Changxun, et al.
Pubblicazione: (2025)

Optimization without Retraction on the Random Generalized Stiefel Manifold
di: Vary, Simon, et al.
Pubblicazione: (2024)

Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning
di: Liu, Yuchen, et al.
Pubblicazione: (2025)

ST-LoRA: Low-rank Adaptation for Spatio-Temporal Forecasting
di: Ruan, Weilin, et al.
Pubblicazione: (2024)

Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
di: Luo, Yuetian, et al.
Pubblicazione: (2021)

Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning
di: Zhai, Zheng, et al.
Pubblicazione: (2026)

StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds
di: Cheema, Prasad, et al.
Pubblicazione: (2024)

Grassmannian Mixture-of-Experts: Concentration-Controlled Routing on Subspace Manifolds
di: Shihab, Ibne Farabi, et al.
Pubblicazione: (2026)

Reducing Communication for Split Learning by Randomized Top-k Sparsification
di: Zheng, Fei, et al.
Pubblicazione: (2023)

Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection
di: Wang, Wenxiao, et al.
Pubblicazione: (2024)

$O(k)$-Equivariant Dimensionality Reduction on Stiefel Manifolds
di: Lee, Andrew, et al.
Pubblicazione: (2023)

AdaRank: Disagreement Based Module Rank Prediction for Low-rank Adaptation
di: Dong, Yihe
Pubblicazione: (2024)

Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
di: Xu, Conglong, et al.
Pubblicazione: (2025)

High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces
di: Nguyen, Quoc-Anh Hoang, et al.
Pubblicazione: (2024)

Subspace Geometry Governs Catastrophic Forgetting in Low-Rank Adaptation
di: Steele, Brady
Pubblicazione: (2026)

Adam Reduces a Unique Form of Sharpness: Theoretical Insights Near the Minimizer Manifold
di: Li, Xinghan, et al.
Pubblicazione: (2025)