Imagen de Portada

Nonlinear Meta-Learning Can Guarantee Faster Rates

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Meunier, Dimitri, Li, Zhu, Gretton, Arthur, Kpotufe, Samory
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Statistics Theory
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Distributionally-Constrained Adversaries in Online Learning
por: Blanchard, Moïse, et al.
Publicado: (2025)

Adaptive Sample Aggregation In Transfer Learning
por: Hanneke, Steve, et al.
Publicado: (2024)

Nonparametric Instrumental Variable Regression with Observed Covariates
por: Shen, Zikai, et al.
Publicado: (2025)

Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
por: Shen, Zikai, et al.
Publicado: (2026)

An Efficient Variant of One-Class SVM with Lifelong Online Learning Guarantees
por: Suk, Joe, et al.
Publicado: (2025)

Regularized least squares learning with heavy-tailed noise is minimax optimal
por: Mollenhauer, Mattes, et al.
Publicado: (2025)

Distribution-Free Rates in Neyman-Pearson Classification
por: Kalan, Mohammadreza M., et al.
Publicado: (2024)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
por: Li, Zhu, et al.
Publicado: (2023)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
por: Meunier, Dimitri, et al.
Publicado: (2024)

Mixed-Sample SGD: an End-to-end Analysis of Supervised Transfer Learning
por: Deng, Yuyang, et al.
Publicado: (2025)

Statistical Learning Guarantees for Group-Invariant Barron Functions
por: Yang, Yahong, et al.
Publicado: (2025)

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
por: Meunier, Dimitri, et al.
Publicado: (2024)

Efficient Inference after Directionally Stable Adaptive Experiments
por: Shen, Zikai, et al.
Publicado: (2026)

Which Spatial Partition Trees are Adaptive to Intrinsic Dimension?
por: Verma, Nakul, et al.
Publicado: (2012)

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
por: Hasson, Hilaf, et al.
Publicado: (2023)

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
por: Kim, Juno, et al.
Publicado: (2025)

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training
por: Wang, Kevin, et al.
Publicado: (2026)

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
por: Wornbard, Jakub, et al.
Publicado: (2026)

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
por: Li, Gen, et al.
Publicado: (2023)

Frequentist Guarantees of Distributed (Non)-Bayesian Inference
por: Wu, Bohan, et al.
Publicado: (2023)

Theoretical Guarantees for the Subspace-Constrained Tyler's Estimator
por: Lerman, Gilad, et al.
Publicado: (2024)

Density Ratio-Free Doubly Robust Proxy Causal Learning
por: Bozkurt, Bariscan, et al.
Publicado: (2025)

Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
por: Peng, Liangzu, et al.
Publicado: (2026)

Density Ratio-based Proxy Causal Learning Without Density Ratios
por: Bozkurt, Bariscan, et al.
Publicado: (2025)

Improved Guarantees for Langevin Monte Carlo with Average Smoothness
por: Dalalyan, Arnak S., et al.
Publicado: (2026)

Sobolev Norm Learning Rates for Conditional Mean Embeddings
por: Talwai, Prem, et al.
Publicado: (2021)

Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks
por: Taheri, Mahsa, et al.
Publicado: (2022)

Faster Diffusion Models via Higher-Order Approximation
por: Li, Gen, et al.
Publicado: (2025)

Meta-Learning and representation learner: A short theoretical note
por: Bouchattaoui, Mouad El
Publicado: (2024)

A Unified Information-Theoretic Framework for Meta-Learning Generalization
por: Wen, Wen, et al.
Publicado: (2025)

Gradient Descent with Projection Finds Over-Parameterized Neural Networks for Learning Low-Degree Polynomials with Nearly Minimax Optimal Rate
por: Yang, Yingzhen, et al.
Publicado: (2026)

Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems
por: Altekrüger, Fabian, et al.
Publicado: (2023)

KL Convergence Guarantees for Score diffusion models under minimal data assumptions
por: Conforti, Giovanni, et al.
Publicado: (2023)

Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
por: Dmitriev, Daniil, et al.
Publicado: (2026)

Benign Overfitting under Learning Rate Conditions for $α$ Sub-exponential Input
por: Okudo, Kota, et al.
Publicado: (2024)

Covariates-Adjusted Mixed-Membership Estimation: A Novel Network Model with Optimal Guarantees
por: Fan, Jianqing, et al.
Publicado: (2025)

$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
por: Fujisawa, Masahiro, et al.
Publicado: (2025)

Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction
por: Tang, Yin, et al.
Publicado: (2024)

Stronger Neyman Regret Guarantees for Adaptive Experimental Design
por: Noarov, Georgy, et al.
Publicado: (2025)

Least Squares Regression Can Exhibit Under-Parameterized Double Descent
por: Li, Xinyue, et al.
Publicado: (2023)