In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
Fuente:
arXiv
Saved in:
| Main Authors: | Philippenko, Constantin, Scaman, Kevin, Massoulié, Laurent |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
by: Philippenko, Constantin, et al.
Published: (2023)
by: Philippenko, Constantin, et al.
Published: (2023)
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
by: Scaman, Kevin, et al.
Published: (2023)
by: Scaman, Kevin, et al.
Published: (2023)
Adaptive collaboration for online personalized distributed learning with heterogeneous clients
by: Philippenko, Constantin, et al.
Published: (2025)
by: Philippenko, Constantin, et al.
Published: (2025)
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
by: Robin, David A. R., et al.
Published: (2025)
by: Robin, David A. R., et al.
Published: (2025)
Variance-Reduced $(\varepsilon,δ)-$Unlearning using Forget Set Gradients
by: Van Waerebeke, Martin, et al.
Published: (2026)
by: Van Waerebeke, Martin, et al.
Published: (2026)
When to Forget? Complexity Trade-offs in Machine Unlearning
by: Van Waerebeke, Martin, et al.
Published: (2025)
by: Van Waerebeke, Martin, et al.
Published: (2025)
Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
by: Giampouras, Paris, et al.
Published: (2024)
by: Giampouras, Paris, et al.
Published: (2024)
Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?
by: Ma, Jianhao, et al.
Published: (2023)
by: Ma, Jianhao, et al.
Published: (2023)
Nonnegative Low-rank Matrix Recovery Can Have Spurious Local Minima
by: Zhang, Richard Y.
Published: (2025)
by: Zhang, Richard Y.
Published: (2025)
Efficient Duple Perturbation Robustness in Low-rank MDPs
by: Hu, Yang, et al.
Published: (2024)
by: Hu, Yang, et al.
Published: (2024)
Heaviside Low-Rank Support Matrix Machine
by: Xiu, Xianchao, et al.
Published: (2026)
by: Xiu, Xianchao, et al.
Published: (2026)
Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems
by: Garber, Dan, et al.
Published: (2022)
by: Garber, Dan, et al.
Published: (2022)
Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems
by: Garber, Dan, et al.
Published: (2022)
by: Garber, Dan, et al.
Published: (2022)
Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime
by: Zhang, Richard Y.
Published: (2021)
by: Zhang, Richard Y.
Published: (2021)
Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?
by: Súkeník, Peter, et al.
Published: (2024)
by: Súkeník, Peter, et al.
Published: (2024)
Efficient Low-rank Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization
by: Wang, Hao, et al.
Published: (2024)
by: Wang, Hao, et al.
Published: (2024)
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
by: Zhuang, Yubo, et al.
Published: (2023)
by: Zhuang, Yubo, et al.
Published: (2023)
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
by: Luo, Yuetian, et al.
Published: (2021)
by: Luo, Yuetian, et al.
Published: (2021)
Disjunctive Branch-and-Bound for Certifiably Optimal Low-Rank Matrix Completion
by: Bertsimas, Dimitris, et al.
Published: (2023)
by: Bertsimas, Dimitris, et al.
Published: (2023)
Refined Analysis of Federated Averaging and Federated Richardson-Romberg
by: Mangold, Paul, et al.
Published: (2024)
by: Mangold, Paul, et al.
Published: (2024)
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
by: Zhang, Weitong, et al.
Published: (2021)
by: Zhang, Weitong, et al.
Published: (2021)
Unbiased Approximate Vector-Jacobian Products for Efficient Backpropagation
by: Bakong, Killian, et al.
Published: (2026)
by: Bakong, Killian, et al.
Published: (2026)
Sparse-ProxSkip: Accelerated Sparse-to-Sparse Training in Federated Learning
by: Meinhardt, Georg, et al.
Published: (2024)
by: Meinhardt, Georg, et al.
Published: (2024)
Robust Low-rank Tensor Train Recovery
by: Qin, Zhen, et al.
Published: (2024)
by: Qin, Zhen, et al.
Published: (2024)
Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings
by: Jiang, Wei, et al.
Published: (2025)
by: Jiang, Wei, et al.
Published: (2025)
Convergence Analysis of the PAGE Stochastic Algorithm for Weakly Convex Finite-Sum Optimization
by: Condat, Laurent, et al.
Published: (2025)
by: Condat, Laurent, et al.
Published: (2025)
A new perspective on low-rank optimization
by: Bertsimas, Dimitris, et al.
Published: (2021)
by: Bertsimas, Dimitris, et al.
Published: (2021)
Endogenous Aggregation of Multiple Data Envelopment Analysis Scores for Large Data Sets
by: Omrani, Hashem, et al.
Published: (2025)
by: Omrani, Hashem, et al.
Published: (2025)
Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
by: Xu, Conglong, et al.
Published: (2025)
by: Xu, Conglong, et al.
Published: (2025)
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
by: Xu, Xingyu, et al.
Published: (2023)
by: Xu, Xingyu, et al.
Published: (2023)
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
by: Zhang, Chenyu, et al.
Published: (2024)
by: Zhang, Chenyu, et al.
Published: (2024)
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
by: Cohen, Jeremy E., et al.
Published: (2024)
by: Cohen, Jeremy E., et al.
Published: (2024)
Sharpness of Minima in Deep Matrix Factorization
by: Kamber, Anil, et al.
Published: (2025)
by: Kamber, Anil, et al.
Published: (2025)
Sampling from Boltzmann densities with physics informed low-rank formats
by: Hagemann, Paul, et al.
Published: (2024)
by: Hagemann, Paul, et al.
Published: (2024)
The power of small initialization in noisy low-tubal-rank tensor recovery
by: Liu, ZHiyu, et al.
Published: (2026)
by: Liu, ZHiyu, et al.
Published: (2026)
MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation
by: Shen, Wei, et al.
Published: (2025)
by: Shen, Wei, et al.
Published: (2025)
Sum-of-norms regularized Nonnegative Matrix Factorization
by: Ang, Andersen, et al.
Published: (2024)
by: Ang, Andersen, et al.
Published: (2024)
Convergence of Alternating Gradient Descent for Matrix Factorization
by: Ward, Rachel, et al.
Published: (2023)
by: Ward, Rachel, et al.
Published: (2023)
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
by: Wang, Po-Wei, et al.
Published: (2017)
by: Wang, Po-Wei, et al.
Published: (2017)
Implicit Bias in Matrix Factorization and its Explicit Realization in a New Architecture
by: Hou, Yikun, et al.
Published: (2025)
by: Hou, Yikun, et al.
Published: (2025)
Similar Items
-
Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
by: Philippenko, Constantin, et al.
Published: (2023) -
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
by: Scaman, Kevin, et al.
Published: (2023) -
Adaptive collaboration for online personalized distributed learning with heterogeneous clients
by: Philippenko, Constantin, et al.
Published: (2025) -
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
by: Robin, David A. R., et al.
Published: (2025) -
Variance-Reduced $(\varepsilon,δ)-$Unlearning using Forget Set Gradients
by: Van Waerebeke, Martin, et al.
Published: (2026)