Cover Image

Optimal In-context Adaptivity and Distributional Robustness of Transformers

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ma, Tianyi, Wang, Tengyao, Samworth, Richard J.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Statistics Theory 62G08, 68T07
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers
by: Ching, Michelle, et al.
Published: (2026)

Deep learning with missing data
by: Ma, Tianyi, et al.
Published: (2025)

Optimal Rate of Kernel Regression in Large Dimensions
by: Lu, Weihao, et al.
Published: (2023)

Wasserstein Distributionally Robust Nonparametric Regression
by: Liu, Changyu, et al.
Published: (2025)

Generalized Factor Neural Network Model for High-dimensional Regression
by: Guo, Zichuan, et al.
Published: (2025)

The Optimality of Kernel Classifiers in Sobolev Space
by: Lai, Jianfa, et al.
Published: (2024)

Distribution estimation via Flow Matching with Lipschitz guarantees
by: Kunkel, Lea
Published: (2025)

Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection
by: Luo, Hengrui, et al.
Published: (2024)

TILT: Target-induced loss tilting under covariate shift
by: Yamamoto, Kakei, et al.
Published: (2026)

On the Statistical Optimality of Optimal Decision Trees
by: Xu, Zineng, et al.
Published: (2026)

Estimating Unbounded Density Ratios: Applications in Error Control under Covariate Shift
by: Xu, Shuntuo, et al.
Published: (2025)

Generalization Error of GAN from the Discriminator's Perspective
by: Yang, Hongkang, et al.
Published: (2021)

On the Convergence of the ELBO to Entropy Sums
by: Lücke, Jörg, et al.
Published: (2022)

Generative Models with ELBOs Converging to Entropy Sums
by: Warnken, Jan, et al.
Published: (2024)

On the minimax optimality of Flow Matching through the connection to kernel density estimation
by: Kunkel, Lea, et al.
Published: (2025)

From Conditional to Unconditional Independence: Testing Conditional Independence via Transport Maps
by: He, Chenxuan, et al.
Published: (2025)

Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints
by: Cai, T. Tony, et al.
Published: (2024)

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

A PAC-Bayes oracle inequality for sparse neural networks
by: Steffen, Maximilian F., et al.
Published: (2022)

Nonparametric estimation of a factorizable density using diffusion models
by: Kwon, Hyeok Kyu, et al.
Published: (2025)

Understanding the Effect of GCN Convolutions in Regression Tasks
by: Chen, Juntong, et al.
Published: (2024)

A Wasserstein perspective of Vanilla GANs
by: Kunkel, Lea, et al.
Published: (2024)

Convergence rates of non-stationary and deep Gaussian process regression
by: Osborne, Conor, et al.
Published: (2023)

Optimal Federated Learning for Functional Mean Estimation under Heterogeneous Privacy Constraints
by: Cai, Tony, et al.
Published: (2024)

Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
by: Auddy, Arnab, et al.
Published: (2024)

The Cost of Adaptation under Differential Privacy: Optimal Adaptive Federated Density Estimation
by: Cai, T. Tony, et al.
Published: (2025)

On robust recovery of signals from indirect observations
by: Bekri, Yannis, et al.
Published: (2025)

Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators
by: Tan, Yan Shuo, et al.
Published: (2024)

Composite Lp-quantile regression, near quantile regression and the oracle model selection theory
by: Mou, Fuming Lin WEilin
Published: (2025)

Multi-Head Attention as Ensemble Nadaraya-Watson Estimation: Variance Reduction, Decorrelation, and Optimal Head Diversity
by: Fokoué, Ernest
Published: (2026)

Covering Numbers for Deep ReLU Networks with Applications to Function Approximation and Nonparametric Regression
by: Ou, Weigutian, et al.
Published: (2024)

Nonparametric inference under shape constraints: past, present and future
by: Samworth, Richard J.
Published: (2025)

A supervised deep learning method for nonparametric density estimation
by: Bos, Thijs, et al.
Published: (2023)

An operator learning perspective on parameter-to-observable maps
by: Huang, Daniel Zhengyu, et al.
Published: (2024)

Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels
by: Huang, Dongming, et al.
Published: (2025)

Estimating a Function and Its Derivatives Under a Smoothness Condition
by: Lim, Eunji
Published: (2024)

Provable Adversarial Robustness in In-Context Learning
by: Zhang, Di
Published: (2026)

Model-free filtering in high dimensions via projection and score-based diffusions
by: Christensen, Sören, et al.
Published: (2025)

Bayesian Neural Networks vs. Mixture Density Networks: Theoretical and Empirical Insights for Uncertainty-Aware Nonlinear Modeling
by: Ghosh, Riddhi Pratim, et al.
Published: (2025)

What Functions Does XGBoost Learn?
by: Ki, Dohyeong, et al.
Published: (2026)