Saved in:
| Main Authors: | Wortsman, Arie, Loureiro, Bruno |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.04780 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Random Matrix Theory of Masked Self-Supervised Regression
by: Zurich, Arie Wortsman, et al.
Published: (2026)
by: Zurich, Arie Wortsman, et al.
Published: (2026)
Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
by: Wortsman-Zurich, Arie, et al.
Published: (2026)
by: Wortsman-Zurich, Arie, et al.
Published: (2026)
Dimension-free deterministic equivalents and scaling laws for random feature regression
by: Defilippis, Leonardo, et al.
Published: (2024)
by: Defilippis, Leonardo, et al.
Published: (2024)
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
by: Adomaityte, Urte, et al.
Published: (2023)
by: Adomaityte, Urte, et al.
Published: (2023)
Universality laws for Gaussian mixtures in generalized linear models
by: Dandi, Yatin, et al.
Published: (2023)
by: Dandi, Yatin, et al.
Published: (2023)
Uniform convergence for Gaussian kernel ridge regression
by: Dommel, Paul, et al.
Published: (2025)
by: Dommel, Paul, et al.
Published: (2025)
Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data
by: Dempster, Angus, et al.
Published: (2024)
by: Dempster, Angus, et al.
Published: (2024)
Optimal scaling laws in learning hierarchical multi-index models
by: Defilippis, Leonardo, et al.
Published: (2026)
by: Defilippis, Leonardo, et al.
Published: (2026)
Dimension free ridge regression
by: Cheng, Chen, et al.
Published: (2022)
by: Cheng, Chen, et al.
Published: (2022)
Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
by: Calandriello, Daniele, et al.
Published: (2026)
by: Calandriello, Daniele, et al.
Published: (2026)
Risk and cross validation in ridge regression with correlated samples
by: Atanasov, Alexander, et al.
Published: (2024)
by: Atanasov, Alexander, et al.
Published: (2024)
Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD
by: Arnaboldi, Luca, et al.
Published: (2023)
by: Arnaboldi, Luca, et al.
Published: (2023)
High-dimensional analysis of ridge regression for non-identically distributed data with a variance profile
by: Bigot, Jérémie, et al.
Published: (2024)
by: Bigot, Jérémie, et al.
Published: (2024)
Learning solution operator of dynamical systems with diffusion maps kernel ridge regression
by: Song, Jiwoo, et al.
Published: (2025)
by: Song, Jiwoo, et al.
Published: (2025)
Robust, randomized preconditioning for kernel ridge regression
by: Díaz, Mateo, et al.
Published: (2023)
by: Díaz, Mateo, et al.
Published: (2023)
Breaking the curse of dimensionality for linear rules: optimal predictors over the ellipsoid
by: Ayme, Alexis, et al.
Published: (2025)
by: Ayme, Alexis, et al.
Published: (2025)
High-dimensional ridge regression with random features for non-identically distributed data with a variance profile
by: Dabo, Issa-Mbenard, et al.
Published: (2025)
by: Dabo, Issa-Mbenard, et al.
Published: (2025)
Fast Escape, Slow Convergence: Learning Dynamics of Phase Retrieval under Power-Law Data
by: Braun, Guillaume, et al.
Published: (2025)
by: Braun, Guillaume, et al.
Published: (2025)
Comparing regularisation paths of (conjugate) gradient estimators in ridge regression
by: Hucker, Laura, et al.
Published: (2025)
by: Hucker, Laura, et al.
Published: (2025)
Reproducible scaling laws for contrastive language-image learning
by: Cherti, Mehdi, et al.
Published: (2022)
by: Cherti, Mehdi, et al.
Published: (2022)
Annealing in variational inference mitigates mode collapse: A theoretical study on Gaussian mixtures
by: Fogliani, Luigi, et al.
Published: (2026)
by: Fogliani, Luigi, et al.
Published: (2026)
Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
by: Ahmadi, Sana, et al.
Published: (2024)
by: Ahmadi, Sana, et al.
Published: (2024)
Optimal ridge regularization revisited
by: Timmermans, Jack, et al.
Published: (2026)
by: Timmermans, Jack, et al.
Published: (2026)
Optimal ridge penalty for real-world high-dimensional data can be zero or negative due to the implicit ridge regularization
by: Kobak, Dmitry, et al.
Published: (2018)
by: Kobak, Dmitry, et al.
Published: (2018)
A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models
by: Defilippis, Leonardo, et al.
Published: (2026)
by: Defilippis, Leonardo, et al.
Published: (2026)
Resolving Discrepancies in Compute-Optimal Scaling of Language Models
by: Porian, Tomer, et al.
Published: (2024)
by: Porian, Tomer, et al.
Published: (2024)
How does feature learning reshape the function space?
by: Lobo, João, et al.
Published: (2026)
by: Lobo, João, et al.
Published: (2026)
Learning curves theory for hierarchically compositional data with power-law distributed features
by: Cagnetta, Francesco, et al.
Published: (2025)
by: Cagnetta, Francesco, et al.
Published: (2025)
Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks
by: Huang, Jie, et al.
Published: (2026)
by: Huang, Jie, et al.
Published: (2026)
On the existence of consistent adversarial attacks in high-dimensional linear classification
by: Vilucchio, Matteo, et al.
Published: (2025)
by: Vilucchio, Matteo, et al.
Published: (2025)
Fair regression under localized demographic parity constraints
by: Charpentier, Arthur, et al.
Published: (2026)
by: Charpentier, Arthur, et al.
Published: (2026)
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
by: Dandi, Yatin, et al.
Published: (2023)
by: Dandi, Yatin, et al.
Published: (2023)
Interpretable Tabular Foundation Models via In-Context Kernel Regression
by: Miftachov, Ratmir, et al.
Published: (2026)
by: Miftachov, Ratmir, et al.
Published: (2026)
Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inference
by: Arie, Asaf Ben, et al.
Published: (2025)
by: Arie, Asaf Ben, et al.
Published: (2025)
Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning
by: Arie, Asaf Ben, et al.
Published: (2024)
by: Arie, Asaf Ben, et al.
Published: (2024)
Analysis of Nystrom method with sequential ridge leverage scores
by: Calandriello, Daniele, et al.
Published: (2026)
by: Calandriello, Daniele, et al.
Published: (2026)
The Stochastic Occupation Kernel Method for System Identification
by: Wells, Michael, et al.
Published: (2024)
by: Wells, Michael, et al.
Published: (2024)
Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powers
by: Bai, Sheng-Chen, et al.
Published: (2024)
by: Bai, Sheng-Chen, et al.
Published: (2024)
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
by: Tanner, Kasimir, et al.
Published: (2024)
by: Tanner, Kasimir, et al.
Published: (2024)
A theoretical perspective on mode collapse in variational inference
by: Soletskyi, Roman, et al.
Published: (2024)
by: Soletskyi, Roman, et al.
Published: (2024)
Similar Items
-
A Random Matrix Theory of Masked Self-Supervised Regression
by: Zurich, Arie Wortsman, et al.
Published: (2026) -
Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
by: Wortsman-Zurich, Arie, et al.
Published: (2026) -
Dimension-free deterministic equivalents and scaling laws for random feature regression
by: Defilippis, Leonardo, et al.
Published: (2024) -
High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
by: Adomaityte, Urte, et al.
Published: (2023) -
Universality laws for Gaussian mixtures in generalized linear models
by: Dandi, Yatin, et al.
Published: (2023)