Dimension lower bounds for linear approaches to function approximation
Fuente:
arXiv
Saved in:
| Main Author: | Hsu, Daniel |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
by: Noskov, Fedor, et al.
Published: (2025)
by: Noskov, Fedor, et al.
Published: (2025)
On the attainment of the Wasserstein--Cramer--Rao lower bound
by: Nishimori, Hayato, et al.
Published: (2025)
by: Nishimori, Hayato, et al.
Published: (2025)
High-probability sample complexities for policy evaluation with linear function approximation
by: Li, Gen, et al.
Published: (2023)
by: Li, Gen, et al.
Published: (2023)
The monotonicity of the Franz-Parisi potential is equivalent with Low-degree MMSE lower bounds
by: Tsirkas, Konstantinos, et al.
Published: (2026)
by: Tsirkas, Konstantinos, et al.
Published: (2026)
Sliced gradient-enhanced Kriging for high-dimensional function approximation
by: Cheng, Kai, et al.
Published: (2022)
by: Cheng, Kai, et al.
Published: (2022)
Kullback-Leibler excess risk bounds for exponential weighted aggregation in Generalized linear models
by: Mai, The Tien
Published: (2025)
by: Mai, The Tien
Published: (2025)
Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity
by: Dudeja, Rishabh, et al.
Published: (2022)
by: Dudeja, Rishabh, et al.
Published: (2022)
On the sample complexity of parameter estimation in logistic regression with normal design
by: Hsu, Daniel, et al.
Published: (2023)
by: Hsu, Daniel, et al.
Published: (2023)
High-probability minimax lower bounds
by: Ma, Tianyi, et al.
Published: (2024)
by: Ma, Tianyi, et al.
Published: (2024)
Entrywise error bounds for low-rank approximations of kernel matrices
by: Modell, Alexander
Published: (2024)
by: Modell, Alexander
Published: (2024)
Kernel Two-Sample Tests in High Dimension: Interplay Between Moment Discrepancy and Dimension-and-Sample Orders
by: Yan, Jian, et al.
Published: (2021)
by: Yan, Jian, et al.
Published: (2021)
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
by: Mou, Wenlong, et al.
Published: (2021)
by: Mou, Wenlong, et al.
Published: (2021)
kTULA: A Langevin sampling algorithm with improved KL bounds under super-linear log-gradients
by: Lytras, Iosif, et al.
Published: (2025)
by: Lytras, Iosif, et al.
Published: (2025)
Handling bounded response in high dimensions: a Horseshoe prior Bayesian Beta regression approach
by: Mai, The Tien
Published: (2025)
by: Mai, The Tien
Published: (2025)
Sharp Gaussian approximations for Decentralized Federated Learning
by: Bonnerjee, Soham, et al.
Published: (2025)
by: Bonnerjee, Soham, et al.
Published: (2025)
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
by: Li, Yingru, et al.
Published: (2024)
by: Li, Yingru, et al.
Published: (2024)
Dimension-free Score Matching and Time Bootstrapping for Diffusion Models
by: Kumar, Syamantak, et al.
Published: (2025)
by: Kumar, Syamantak, et al.
Published: (2025)
Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions
by: Lu, Weihao, et al.
Published: (2026)
by: Lu, Weihao, et al.
Published: (2026)
Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions
by: Azangulov, Iskander, et al.
Published: (2024)
by: Azangulov, Iskander, et al.
Published: (2024)
A variational Bayes approach to debiased inference for low-dimensional parameters in high-dimensional linear regression
by: Castillo, Ismaël, et al.
Published: (2024)
by: Castillo, Ismaël, et al.
Published: (2024)
Adaptive sparse variational approximations for Gaussian process regression
by: Nieman, Dennis, et al.
Published: (2025)
by: Nieman, Dennis, et al.
Published: (2025)
Smoothed SGD for quantiles: Bahadur representation and Gaussian approximation
by: Chen, Likai, et al.
Published: (2025)
by: Chen, Likai, et al.
Published: (2025)
On the best approximation by finite Gaussian mixtures
by: Ma, Yun, et al.
Published: (2024)
by: Ma, Yun, et al.
Published: (2024)
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions
by: Koshizuka, Takeshi, et al.
Published: (2025)
by: Koshizuka, Takeshi, et al.
Published: (2025)
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction
by: Tang, Yin, et al.
Published: (2024)
by: Tang, Yin, et al.
Published: (2024)
Computational and statistical lower bounds for low-rank estimation under general inhomogeneous noise
by: De, Debsurya, et al.
Published: (2025)
by: De, Debsurya, et al.
Published: (2025)
Continuous-time reinforcement learning: ellipticity enables model-free value function approximation
by: Mou, Wenlong
Published: (2026)
by: Mou, Wenlong
Published: (2026)
User-friendly introduction to PAC-Bayes bounds
by: Alquier, Pierre
Published: (2021)
by: Alquier, Pierre
Published: (2021)
A Gapped Scale-Sensitive Dimension and Lower Bounds for Offset Rademacher Complexity
by: Jia, Zeyu, et al.
Published: (2025)
by: Jia, Zeyu, et al.
Published: (2025)
Misclassification bounds for PAC-Bayesian sparse deep learning
by: Mai, The Tien
Published: (2024)
by: Mai, The Tien
Published: (2024)
Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
by: Kan, Kelvin, et al.
Published: (2026)
by: Kan, Kelvin, et al.
Published: (2026)
Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise
by: Ding, Xiucai, et al.
Published: (2026)
by: Ding, Xiucai, et al.
Published: (2026)
The Catastrophic Failure of The k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
by: Lederman, Roy R., et al.
Published: (2026)
by: Lederman, Roy R., et al.
Published: (2026)
Closed-form $\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\ell_p$ bias
by: Zhang, Shuofeng, et al.
Published: (2025)
by: Zhang, Shuofeng, et al.
Published: (2025)
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
by: Stankewitz, Bernhard, et al.
Published: (2024)
by: Stankewitz, Bernhard, et al.
Published: (2024)
A unified construction for series representations and finite approximations of completely random measures
by: Lee, Juho, et al.
Published: (2019)
by: Lee, Juho, et al.
Published: (2019)
Jointly Modeling and Clustering Tensors in High Dimensions
by: Cai, Biao, et al.
Published: (2021)
by: Cai, Biao, et al.
Published: (2021)
Universality laws for Gaussian mixtures in generalized linear models
by: Dandi, Yatin, et al.
Published: (2023)
by: Dandi, Yatin, et al.
Published: (2023)
Simultaneous analysis of approximate leave-one-out cross-validation and mean-field inference
by: Bellec, Pierre C
Published: (2025)
by: Bellec, Pierre C
Published: (2025)
Entangled Mean Estimation in High-Dimensions
by: Diakonikolas, Ilias, et al.
Published: (2025)
by: Diakonikolas, Ilias, et al.
Published: (2025)
Similar Items
-
Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
by: Noskov, Fedor, et al.
Published: (2025) -
On the attainment of the Wasserstein--Cramer--Rao lower bound
by: Nishimori, Hayato, et al.
Published: (2025) -
High-probability sample complexities for policy evaluation with linear function approximation
by: Li, Gen, et al.
Published: (2023) -
The monotonicity of the Franz-Parisi potential is equivalent with Low-degree MMSE lower bounds
by: Tsirkas, Konstantinos, et al.
Published: (2026) -
Sliced gradient-enhanced Kriging for high-dimensional function approximation
by: Cheng, Kai, et al.
Published: (2022)