:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Ayme, Alexis, Loureiro, Bruno
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2509.21174
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Handling Covariate Mismatch in Federated Linear Prediction
di: Ayme, Alexis, et al.
Pubblicazione: (2026)

On the existence of consistent adversarial attacks in high-dimensional linear classification
di: Vilucchio, Matteo, et al.
Pubblicazione: (2025)

Random features models: a way to study the success of naive imputation
di: Ayme, Alexis, et al.
Pubblicazione: (2024)

Breaking the curse of dimensionality in structured density estimation
di: Vandermeulen, Robert A., et al.
Pubblicazione: (2024)

Does the Barron space really defy the curse of dimensionality?
di: Schavemaker, Olov
Pubblicazione: (2025)

Conformal prediction for multi-dimensional time series by ellipsoidal sets
di: Xu, Chen, et al.
Pubblicazione: (2024)

Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD
di: Arnaboldi, Luca, et al.
Pubblicazione: (2023)

The curse of random quantum data
di: Zhang, Kaining, et al.
Pubblicazione: (2024)

Universality laws for Gaussian mixtures in generalized linear models
di: Dandi, Yatin, et al.
Pubblicazione: (2023)

Kernel ridge regression under power-law data: spectrum and generalization
di: Wortsman, Arie, et al.
Pubblicazione: (2025)

Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks
di: Huang, Jie, et al.
Pubblicazione: (2026)

Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions
di: Cole, Frank, et al.
Pubblicazione: (2024)

Learning minimal volume uncertainty ellipsoids
di: Alon, Itai, et al.
Pubblicazione: (2024)

High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality
di: Adomaityte, Urte, et al.
Pubblicazione: (2023)

Annealing in variational inference mitigates mode collapse: A theoretical study on Gaussian mixtures
di: Fogliani, Luigi, et al.
Pubblicazione: (2026)

MOCA-HESP: Meta High-dimensional Bayesian Optimization for Combinatorial and Mixed Spaces via Hyper-ellipsoid Partitioning
di: Ngo, Lam, et al.
Pubblicazione: (2025)

Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
di: Clarté, Lucas, et al.
Pubblicazione: (2024)

Bilinear representation mitigates reversal curse and enables consistent model editing
di: Kim, Dong-Kyum, et al.
Pubblicazione: (2025)

Optimal scaling laws in learning hierarchical multi-index models
di: Defilippis, Leonardo, et al.
Pubblicazione: (2026)

A Noise Sensitivity Exponent Controls Large Statistical-to-Computational Gaps in Single- and Multi-Index Models
di: Defilippis, Leonardo, et al.
Pubblicazione: (2026)

Bayes optimal learning in high-dimensional linear regression with network side information
di: Nandy, Sagnik, et al.
Pubblicazione: (2023)

Dimension-free deterministic equivalents and scaling laws for random feature regression
di: Defilippis, Leonardo, et al.
Pubblicazione: (2024)

Fundamental computational limits of weak learnability in high-dimensional multi-index models
di: Troiani, Emanuele, et al.
Pubblicazione: (2024)

Fast Escape, Slow Convergence: Learning Dynamics of Phase Retrieval under Power-Law Data
di: Braun, Guillaume, et al.
Pubblicazione: (2025)

How does feature learning reshape the function space?
di: Lobo, João, et al.
Pubblicazione: (2026)

The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression
di: Hassani, Hamed, et al.
Pubblicazione: (2022)

Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA
di: Zhou, Yuchen, et al.
Pubblicazione: (2023)

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
di: Dandi, Yatin, et al.
Pubblicazione: (2023)

A Random Matrix Theory of Masked Self-Supervised Regression
di: Zurich, Arie Wortsman, et al.
Pubblicazione: (2026)

On the optimal regret of collaborative personalized linear bandits
di: Huang, Bruce, et al.
Pubblicazione: (2025)

Transformers are almost optimal metalearners for linear classification
di: Magen, Roey, et al.
Pubblicazione: (2025)

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
di: Tanner, Kasimir, et al.
Pubblicazione: (2024)

Fitting an ellipsoid to a quadratic number of random points
di: Bandeira, Afonso S., et al.
Pubblicazione: (2023)

A theoretical perspective on mode collapse in variational inference
di: Soletskyi, Roman, et al.
Pubblicazione: (2024)

Interpretable non-linear dimensionality reduction using gaussian weighted linear transformation
di: Bergh, Erik
Pubblicazione: (2025)

Hybrid safe-strong rules for efficient optimization in lasso-type problems
di: Zeng, Yaohui, et al.
Pubblicazione: (2017)

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs
di: Arnaboldi, Luca, et al.
Pubblicazione: (2024)

The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling
di: Mattei, Pierre-Alexandre, et al.
Pubblicazione: (2026)

On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds
di: Vilucchio, Matteo, et al.
Pubblicazione: (2024)

Asymptotics of Learning with Deep Structured (Random) Features
di: Schröder, Dominik, et al.
Pubblicazione: (2024)