Weighted least-squares approximation with determinantal point processes and generalized volume sampling
Fuente:
arXiv
Saved in:
| Main Authors: | Nouy, Anthony, Michel, Bertrand |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Boosted optimal weighted least-squares
by: Haberstich, Cécile, et al.
Published: (2019)
by: Haberstich, Cécile, et al.
Published: (2019)
Optimal sampling for least squares approximation with general dictionaries
by: Trunschke, Philipp, et al.
Published: (2024)
by: Trunschke, Philipp, et al.
Published: (2024)
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
by: Bolin, David, et al.
Published: (2024)
by: Bolin, David, et al.
Published: (2024)
Optimal sampling for least-squares approximation
by: Adcock, Ben
Published: (2024)
by: Adcock, Ben
Published: (2024)
Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
by: Geraci, Gianluca, et al.
Published: (2025)
by: Geraci, Gianluca, et al.
Published: (2025)
Gaussian Process Regression under Computational and Epistemic Misspecification
by: Sanz-Alonso, Daniel, et al.
Published: (2023)
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)
by: Lei, Guanhang, et al.
Published: (2023)
Score Operator Newton transport
by: Chandramoorthy, Nisha, et al.
Published: (2023)
by: Chandramoorthy, Nisha, et al.
Published: (2023)
Towards Sharp Minimax Risk Bounds for Operator Learning
by: Adcock, Ben, et al.
Published: (2025)
by: Adcock, Ben, et al.
Published: (2025)
Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures
by: Li, Gen, et al.
Published: (2025)
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)
by: Yakovlev, Konstantin, et al.
Published: (2025)
Faster Diffusion Models via Higher-Order Approximation
by: Li, Gen, et al.
Published: (2025)
by: Li, Gen, et al.
Published: (2025)
On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms
by: Auddy, Arnab, et al.
Published: (2025)
by: Auddy, Arnab, 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)
by: Feng, Jinchao, et al.
Published: (2025)
Posterior Covariance Structures in Gaussian Processes
by: Cai, Difeng, et al.
Published: (2024)
by: Cai, Difeng, et al.
Published: (2024)
Optimal Recovery Meets Minimax Estimation
by: DeVore, Ronald, et al.
Published: (2025)
by: DeVore, Ronald, et al.
Published: (2025)
Convergence Rates for Learning Pseudo-Differential Operators
by: Chen, Jiaheng, et al.
Published: (2026)
by: Chen, Jiaheng, et al.
Published: (2026)
Can Linear Probes Measure LLM Uncertainty?
by: Dakhmouche, Ramzi, et al.
Published: (2025)
by: Dakhmouche, Ramzi, et al.
Published: (2025)
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
by: Shen, Zhaiming, et al.
Published: (2025)
by: Shen, Zhaiming, et al.
Published: (2025)
Posterior Concentration of Bayesian Physics-Informed Neural Networks for Elliptic PDEs
by: Zhao, Yuxuan, et al.
Published: (2026)
by: Zhao, Yuxuan, et al.
Published: (2026)
Approximation and learning with compositional tensor trains
by: Eigel, Martin, et al.
Published: (2025)
by: Eigel, Martin, et al.
Published: (2025)
A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
by: Yang, Jia-Qi, et al.
Published: (2025)
by: Yang, Jia-Qi, 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)
by: Lu, Yiping, et al.
Published: (2024)
Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias
by: Wong, Honam, et al.
Published: (2024)
by: Wong, Honam, et al.
Published: (2024)
Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model
by: Tang, Runshi, et al.
Published: (2023)
by: Tang, Runshi, et al.
Published: (2023)
Stein transport for Bayesian inference
by: Nüsken, Nikolas
Published: (2024)
by: Nüsken, Nikolas
Published: (2024)
Gaussian Processes and Reproducing Kernels: Connections and Equivalences
by: Kanagawa, Motonobu, et al.
Published: (2025)
by: Kanagawa, Motonobu, et al.
Published: (2025)
Samplet limits and multiwavelets
by: Giacchi, Gianluca, et al.
Published: (2026)
by: Giacchi, Gianluca, et al.
Published: (2026)
Analysis of singular subspaces under random perturbations
by: Wang, Ke
Published: (2024)
by: Wang, Ke
Published: (2024)
Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges
by: Auddy, Arnab, et al.
Published: (2024)
by: Auddy, Arnab, et al.
Published: (2024)
Revisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence
by: Tang, Runshi, et al.
Published: (2025)
by: Tang, Runshi, et al.
Published: (2025)
Provable Diffusion Posterior Sampling for Bayesian Inversion
by: Chang, Jinyuan, et al.
Published: (2025)
by: Chang, Jinyuan, et al.
Published: (2025)
Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity
by: Luo, Hengrui, et al.
Published: (2026)
by: Luo, Hengrui, et al.
Published: (2026)
Perturbation Analysis of Randomized SVD and its Applications to Statistics
by: Zhang, Yichi, et al.
Published: (2022)
by: Zhang, Yichi, et al.
Published: (2022)
Convergence of Kinetic Langevin Monte Carlo on Lie groups
by: Kong, Lingkai, et al.
Published: (2024)
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)
by: Ghattas, Omar Al, et al.
Published: (2022)
Probabilistic Analysis of Least Squares, Orthogonal Projection, and QR Factorization Algorithms Subject to Gaussian Noise
by: Lotfi, Ali, et al.
Published: (2024)
by: Lotfi, Ali, et al.
Published: (2024)
Geometric optics approximation sampling: near-field case
by: Sun, Zejun, et al.
Published: (2024)
by: Sun, Zejun, et al.
Published: (2024)
Hybrid least squares for learning functions from highly noisy data
by: Adcock, Ben, et al.
Published: (2025)
by: Adcock, Ben, et al.
Published: (2025)
Regularized Stein Variational Gradient Flow
by: He, Ye, et al.
Published: (2022)
by: He, Ye, et al.
Published: (2022)
Similar Items
-
Boosted optimal weighted least-squares
by: Haberstich, Cécile, et al.
Published: (2019) -
Optimal sampling for least squares approximation with general dictionaries
by: Trunschke, Philipp, et al.
Published: (2024) -
Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
by: Bolin, David, et al.
Published: (2024) -
Optimal sampling for least-squares approximation
by: Adcock, Ben
Published: (2024) -
Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
by: Geraci, Gianluca, et al.
Published: (2025)