Image de couverture de livre

Linear Recursive Feature Machines provably recover low-rank matrices

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Radhakrishnan, Adityanarayanan, Belkin, Mikhail, Drusvyatskiy, Dmitriy
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
par: Zhu, Libin, et autres
Publié: (2026)

Context-Scaling versus Task-Scaling in In-Context Learning
par: Abedsoltan, Amirhesam, et autres
Publié: (2024)

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
par: Beaglehole, Daniel, et autres
Publié: (2025)

Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning
par: Zhu, Libin, et autres
Publié: (2023)

Quadratic models for understanding catapult dynamics of neural networks
par: Zhu, Libin, et autres
Publié: (2022)

Toward universal steering and monitoring of AI models
par: Beaglehole, Daniel, et autres
Publié: (2025)

Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
par: Mallinar, Neil, et autres
Publié: (2024)

The Weight Gram Matrix Captures Sequential Feature Linearization in Deep Networks
par: Cha, Taehun, et autres
Publié: (2026)

A short proof of near-linear convergence of adaptive gradient descent under fourth-order growth and convexity
par: Davis, Damek, et autres
Publié: (2026)

When do spectral gradient updates help in deep learning?
par: Davis, Damek, et autres
Publié: (2025)

Contextual Linear Activation Steering of Language Models
par: Hsu, Brandon, et autres
Publié: (2026)

Efficient and accurate steering of Large Language Models through attention-guided feature learning
par: Davarmanesh, Parmida, et autres
Publié: (2026)

High-dimensional Limit of SGD for Diagonal Linear Networks
par: Malaxechebarría, Begoña García, et autres
Publié: (2026)

Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality
par: Cutler, Joshua, et autres
Publié: (2022)

Iteratively reweighted kernel machines efficiently learn sparse functions
par: Zhu, Libin, et autres
Publié: (2025)

Gradient descent with adaptive stepsize converges (nearly) linearly under fourth-order growth
par: Davis, Damek, et autres
Publié: (2024)

Invariant Kernels: Rank Stabilization and Generalization Across Dimensions
par: Díaz, Mateo, et autres
Publié: (2025)

The radius of statistical efficiency
par: Cutler, Joshua, et autres
Publié: (2024)

On the Nystrom Approximation for Preconditioning in Kernel Machines
par: Abedsoltan, Amirhesam, et autres
Publié: (2023)

Breaking Data Symmetry is Needed For Generalization in Feature Learning Kernels
par: Bernal, Marcel Tomàs, et autres
Publié: (2026)

Shallow diffusion networks provably learn hidden low-dimensional structure
par: Boffi, Nicholas M., et autres
Publié: (2024)

Online Covariance Estimation in Nonsmooth Stochastic Approximation
par: Jiang, Liwei, et autres
Publié: (2025)

More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
par: Simon, James B., et autres
Publié: (2023)

Computational and statistical lower bounds for low-rank estimation under general inhomogeneous noise
par: De, Debsurya, et autres
Publié: (2025)

The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations
par: Boix-Adsera, Enric, et autres
Publié: (2025)

Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probing
par: Mirtaheri, Parsa, et autres
Publié: (2026)

Entrywise error bounds for low-rank approximations of kernel matrices
par: Modell, Alexander
Publié: (2024)

On provable privacy vulnerabilities of graph representations
par: Wu, Ruofan, et autres
Publié: (2024)

Mirror Descent on Reproducing Kernel Banach Spaces
par: Kumar, Akash, et autres
Publié: (2024)

General and Efficient Steering of Unconditional Diffusion
par: Wang, Qingsong, et autres
Publié: (2026)

A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis
par: Kumar, Akash, et autres
Publié: (2025)

When big data actually are low-rank, or entrywise approximation of certain function-generated matrices
par: Budzinskiy, Stanislav
Publié: (2024)

Recursive State Inference for Linear PASFA
par: Rishi, Vishal
Publié: (2025)

Fast training of large kernel models with delayed projections
par: Abedsoltan, Amirhesam, et autres
Publié: (2024)

Average gradient outer product as a mechanism for deep neural collapse
par: Beaglehole, Daniel, et autres
Publié: (2024)

Localization from structured distance matrices via low-rank matrix recovery
par: Lichtenberg, Samuel, et autres
Publié: (2023)

Steering Autoregressive Music Generation with Recursive Feature Machines
par: Zhao, Daniel, et autres
Publié: (2025)

Attention layers provably solve single-location regression
par: Marion, Pierre, et autres
Publié: (2024)

Learning efficient and provably convergent splitting methods
par: Kreusser, L. M., et autres
Publié: (2024)

Amortised and provably-robust simulation-based inference
par: Bharti, Ayush, et autres
Publié: (2026)