The phase diagram of kernel interpolation in large dimensions
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Haobo, Lu, Weihao, Lin, Qian |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Pinsker bound of inner product kernel regression in large dimensions
by: Lu, Weihao, et al.
Published: (2024)
by: Lu, Weihao, et al.
Published: (2024)
On the Optimality of Misspecified Spectral Algorithms
by: Zhang, Haobo, et al.
Published: (2023)
by: Zhang, Haobo, et al.
Published: (2023)
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021)
by: Cheng, Xiuyuan, et al.
Published: (2021)
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)
To bootstrap or to rollout? An optimal and adaptive interpolation
by: Mou, Wenlong, et al.
Published: (2024)
by: Mou, Wenlong, et al.
Published: (2024)
Optimal Rate of Kernel Regression in Large Dimensions
by: Lu, Weihao, et al.
Published: (2023)
by: Lu, Weihao, et al.
Published: (2023)
Towards a Statistical Understanding of Neural Networks: Beyond the Neural Tangent Kernel Theories
by: Zhang, Haobo, et al.
Published: (2024)
by: Zhang, Haobo, et al.
Published: (2024)
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
by: Haas, Moritz, et al.
Published: (2023)
by: Haas, Moritz, et al.
Published: (2023)
On damage of interpolation to adversarial robustness in regression
by: Peng, Jingfu, et al.
Published: (2026)
by: Peng, Jingfu, et al.
Published: (2026)
Physics-informed kernel learning
by: Doumèche, Nathan, et al.
Published: (2024)
by: Doumèche, Nathan, et al.
Published: (2024)
On the number of modes of Gaussian kernel density estimators
by: Geshkovski, Borjan, et al.
Published: (2024)
by: Geshkovski, Borjan, et al.
Published: (2024)
Early stopping and polynomial smoothing in regression with reproducing kernels
by: Averyanov, Yaroslav, et al.
Published: (2020)
by: Averyanov, Yaroslav, et al.
Published: (2020)
Scalable and adaptive prediction bands with kernel sum-of-squares
by: Allain, Louis, et al.
Published: (2025)
by: Allain, Louis, et al.
Published: (2025)
Ridge interpolators in correlated factor regression models -- exact risk analysis
by: Stojnic, Mihailo
Published: (2024)
by: Stojnic, Mihailo
Published: (2024)
Generalization error of min-norm interpolators in transfer learning
by: Song, Yanke, et al.
Published: (2024)
by: Song, Yanke, et al.
Published: (2024)
Gradient dynamics for low-rank fine-tuning beyond kernels
by: Dayi, Arif Kerem, et al.
Published: (2024)
by: Dayi, Arif Kerem, et al.
Published: (2024)
Consistent estimation of generative model representations in the data kernel perspective space
by: Acharyya, Aranyak, et al.
Published: (2024)
by: Acharyya, Aranyak, et al.
Published: (2024)
Effective regions and kernels in continuous sparse regularisation, with application to sketched mixtures
by: De Castro, Yohann, et al.
Published: (2025)
by: De Castro, Yohann, et al.
Published: (2025)
Improved learning theory for kernel distribution regression with two-stage sampling
by: Bachoc, François, et al.
Published: (2023)
by: Bachoc, François, et al.
Published: (2023)
Precise analysis of ridge interpolators under heavy correlations -- a Random Duality Theory view
by: Stojnic, Mihailo
Published: (2024)
by: Stojnic, Mihailo
Published: (2024)
On consistent estimation of dimension values
by: Cholaquidis, Alejandro, et al.
Published: (2024)
by: Cholaquidis, Alejandro, et al.
Published: (2024)
Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
by: Zhu, Libin, et al.
Published: (2026)
by: Zhu, Libin, et al.
Published: (2026)
Statistical learning on measures: an application to persistence diagrams
by: Hacquard, Olympio, et al.
Published: (2023)
by: Hacquard, Olympio, et al.
Published: (2023)
Optimal Confidence Band for Kernel Gradient Flow Estimator
by: Cheng, Yuqian, et al.
Published: (2026)
by: Cheng, Yuqian, et al.
Published: (2026)
Fast kernel methods: Sobolev, physics-informed, and additive models
by: Doumèche, Nathan, et al.
Published: (2025)
by: Doumèche, Nathan, et al.
Published: (2025)
Compressed Empirical Measures (in finite dimensions)
by: Grünewälder, Steffen
Published: (2022)
by: Grünewälder, Steffen
Published: (2022)
High-accuracy and dimension-free sampling with diffusions
by: Gatmiry, Khashayar, et al.
Published: (2026)
by: Gatmiry, Khashayar, et al.
Published: (2026)
Generalization Error Curves for Analytic Spectral Algorithms under Power-law Decay
by: Li, Yicheng, et al.
Published: (2024)
by: Li, Yicheng, et al.
Published: (2024)
On the VC dimension of deep group convolutional neural networks
by: Sepliarskaia, Anna, et al.
Published: (2024)
by: Sepliarskaia, Anna, et al.
Published: (2024)
Functional Linear Regression of Cumulative Distribution Functions
by: Zhang, Qian, et al.
Published: (2022)
by: Zhang, Qian, et al.
Published: (2022)
Iteratively reweighted kernel machines efficiently learn sparse functions
by: Zhu, Libin, et al.
Published: (2025)
by: Zhu, Libin, et al.
Published: (2025)
Local minima of the empirical risk in high dimension: General theorems and convex examples
by: Asgari, Kiana, et al.
Published: (2025)
by: Asgari, Kiana, et al.
Published: (2025)
Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables
by: Gui, Yu, et al.
Published: (2025)
by: Gui, Yu, et al.
Published: (2025)
High dimensional theory of two-phase optimizers
by: Agarwala, Atish
Published: (2026)
by: Agarwala, Atish
Published: (2026)
A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces
by: Wynne, George, et al.
Published: (2022)
by: Wynne, George, et al.
Published: (2022)
Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis
by: Han, Sangil, et al.
Published: (2024)
by: Han, Sangil, et al.
Published: (2024)
Efficient Group Lasso Regularized Rank Regression with Data-Driven Parameter Determination
by: Lin, Meixia, et al.
Published: (2025)
by: Lin, Meixia, et al.
Published: (2025)
Low-degree Lower bounds for clustering in moderate dimension
by: Carpentier, Alexandra, et al.
Published: (2026)
by: Carpentier, Alexandra, et al.
Published: (2026)
On the distance between mean and geometric median in high dimensions
by: Schwank, Richard, et al.
Published: (2025)
by: Schwank, Richard, et al.
Published: (2025)
Efficient mapping of phase diagrams with conditional Boltzmann Generators
by: Schebek, Maximilian, et al.
Published: (2024)
by: Schebek, Maximilian, et al.
Published: (2024)
Similar Items
-
On the Pinsker bound of inner product kernel regression in large dimensions
by: Lu, Weihao, et al.
Published: (2024) -
On the Optimality of Misspecified Spectral Algorithms
by: Zhang, Haobo, et al.
Published: (2023) -
Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021) -
Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions
by: Lu, Weihao, et al.
Published: (2026) -
To bootstrap or to rollout? An optimal and adaptive interpolation
by: Mou, Wenlong, et al.
Published: (2024)