Copertina

Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ma, Jianhao, Fattahi, Salar
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Optimization and Control
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
di: Ma, Jianhao, et al.
Pubblicazione: (2024)

Understanding the Implicit Regularization of Gradient Descent in Over-parameterized Models
di: Ma, Jianhao, et al.
Pubblicazione: (2025)

Wasserstein Distributionally Robust Online Learning
di: Chen, Guixian, et al.
Pubblicazione: (2026)

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

Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
di: Zhang, Gavin, et al.
Pubblicazione: (2025)

Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
di: Zhang, Gavin, et al.
Pubblicazione: (2022)

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

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

Simple Alternating Minimization Provably Solves Complete Dictionary Learning
di: Liang, Geyu, et al.
Pubblicazione: (2022)

Efficient Duple Perturbation Robustness in Low-rank MDPs
di: Hu, Yang, et al.
Pubblicazione: (2024)

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

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

RANSAC Revisited: An Improved Algorithm for Robust Subspace Recovery under Adversarial and Noisy Corruptions
di: Chen, Guixian, et al.
Pubblicazione: (2025)

Disjunctive Branch-and-Bound for Certifiably Optimal Low-Rank Matrix Completion
di: Bertsimas, Dimitris, et al.
Pubblicazione: (2023)

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
di: Zhuang, Yubo, et al.
Pubblicazione: (2023)

Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?
di: Súkeník, Peter, et al.
Pubblicazione: (2024)

A Smoothing Newton Method for Rank-one Matrix Recovery
di: Maunu, Tyler, et al.
Pubblicazione: (2025)

Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles
di: Forel, Alexandre, et al.
Pubblicazione: (2022)

Quantization through Piecewise-Affine Regularization: Optimization and Statistical Guarantees
di: Ma, Jianhao, et al.
Pubblicazione: (2025)

Learning Optimal Classification Trees Robust to Distribution Shifts
di: Justin, Nathan, et al.
Pubblicazione: (2023)

Optimal Projection-Free Adaptive SGD for Matrix Optimization
di: Kovalev, Dmitry
Pubblicazione: (2026)

Heaviside Low-Rank Support Matrix Machine
di: Xiu, Xianchao, et al.
Pubblicazione: (2026)

Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems
di: Garber, Dan, et al.
Pubblicazione: (2022)

Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems
di: Garber, Dan, et al.
Pubblicazione: (2022)

Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
di: Anderson, Brendon G., et al.
Pubblicazione: (2021)

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

Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery
di: Lerman, Gilad, et al.
Pubblicazione: (2025)

The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
di: Xu, Xingyu, et al.
Pubblicazione: (2023)

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

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

Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
di: Calo, Sergio, et al.
Pubblicazione: (2024)

A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
di: Zhao, Heyang, et al.
Pubblicazione: (2023)

Implicit Regularization Makes Overparameterized Asymmetric Matrix Sensing Robust to Perturbations
di: Wind, Johan S.
Pubblicazione: (2023)

PARQ: Piecewise-Affine Regularized Quantization
di: Jin, Lisa, et al.
Pubblicazione: (2025)

Optimal Rates for Robust Stochastic Convex Optimization
di: Gao, Changyu, et al.
Pubblicazione: (2024)

Zeroth-order Low-rank Hessian Estimation via Matrix Recovery
di: Wang, Tianyu, et al.
Pubblicazione: (2024)

Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
di: Zhang, Weitong, et al.
Pubblicazione: (2021)

Modified K-means Algorithm with Local Optimality Guarantees
di: Li, Mingyi, et al.
Pubblicazione: (2025)

Optimal Local Convergence Rates of Stochastic First-Order Methods under Local $α$-PL
di: Masiha, Saeed, et al.
Pubblicazione: (2024)

Unifying Distributionally Robust Optimization via Optimal Transport Theory
di: Blanchet, Jose, et al.
Pubblicazione: (2023)