Cover Image

Towards Sharp Minimax Risk Bounds for Operator Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Adcock, Ben, Maier, Gregor, Parhi, Rahul
Format:	Preprint
Published:	2025
Subjects:	Statistics Theory Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimal Recovery Meets Minimax Estimation
by: DeVore, Ronald, et al.
Published: (2025)

The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
by: Adcock, Ben, et al.
Published: (2024)

Convergence Rates for Learning Pseudo-Differential Operators
by: Chen, Jiaheng, et al.
Published: (2026)

Score Operator Newton transport
by: Chandramoorthy, Nisha, et al.
Published: (2023)

A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
by: Yang, Jia-Qi, et al.
Published: (2025)

Optimal sampling for least-squares approximation
by: Adcock, Ben
Published: (2024)

A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models
by: Li, Gen, et al.
Published: (2024)

On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms
by: Auddy, Arnab, et al.
Published: (2025)

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
by: Shen, Zhaiming, et al.
Published: (2025)

Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications
by: Feng, Jinchao, et al.
Published: (2025)

On the Upper Bounds for the Matrix Spectral Norm
by: Naumov, Alexey, et al.
Published: (2025)

Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
by: Geraci, Gianluca, et al.
Published: (2025)

Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures
by: Li, Gen, et al.
Published: (2025)

Simultaneous Approximation of the Score Function and Its Derivatives by Deep Neural Networks
by: Yakovlev, Konstantin, et al.
Published: (2025)

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

Can Linear Probes Measure LLM Uncertainty?
by: Dakhmouche, Ramzi, et al.
Published: (2025)

Posterior Covariance Structures in Gaussian Processes
by: Cai, Difeng, et al.
Published: (2024)

Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
by: Bolin, David, et al.
Published: (2024)

Gaussian Process Regression under Computational and Epistemic Misspecification
by: Sanz-Alonso, Daniel, et al.
Published: (2023)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
by: Lei, Guanhang, et al.
Published: (2023)

Weighted least-squares approximation with determinantal point processes and generalized volume sampling
by: Nouy, Anthony, et al.
Published: (2023)

Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
by: Zhao, Yuxuan, et al.
Published: (2026)

Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias
by: Wong, Honam, et al.
Published: (2024)

Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis
by: Paik, Seunghoon, et al.
Published: (2025)

Which Spaces can be Embedded in $L_p$-type Reproducing Kernel Banach Space? A Characterization via Metric Entropy
by: Lu, Yiping, et al.
Published: (2024)

Perturbation Bounds for Low-Rank Inverse Approximations under Noise
by: Tran, Phuc, et al.
Published: (2025)

Characteristic Learning for Provable One Step Generation
by: Ding, Zhao, et al.
Published: (2024)

Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
by: Adcock, Ben, et al.
Published: (2024)

Optimal deep learning of holomorphic operators between Banach spaces
by: Adcock, Ben, et al.
Published: (2024)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences
by: Kanagawa, Motonobu, et al.
Published: (2025)

Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence
by: Tang, Runshi, et al.
Published: (2025)

Provable Diffusion Posterior Sampling for Bayesian Inversion
by: Chang, Jinyuan, et al.
Published: (2025)

Stein transport for Bayesian inference
by: Nüsken, Nikolas
Published: (2024)

Samplet limits and multiwavelets
by: Giacchi, Gianluca, et al.
Published: (2026)

Analysis of singular subspaces under random perturbations
by: Wang, Ke
Published: (2024)

Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges
by: Auddy, Arnab, et al.
Published: (2024)

Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity
by: Luo, Hengrui, et al.
Published: (2026)

Perturbation Analysis of Randomized SVD and its Applications to Statistics
by: Zhang, Yichi, et al.
Published: (2022)

Convergence of Kinetic Langevin Monte Carlo on Lie groups
by: Kong, Lingkai, et al.
Published: (2024)

Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization
by: Ghattas, Omar Al, et al.
Published: (2022)