Cover Image

Compressed Empirical Measures (in finite dimensions)

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Grünewälder, Steffen
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Estimating the Mixing Coefficients of Geometrically Ergodic Markov Processes
by: Grünewälder, Steffen, et al.
Published: (2024)

Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters
by: Chernobrovkina, Daryna, et al.
Published: (2025)

On the Variance, Admissibility, and Stability of Empirical Risk Minimization
by: Kur, Gil, et al.
Published: (2023)

A Researcher's Guide to Empirical Risk Minimization
by: van der Laan, Lars
Published: (2026)

On consistent estimation of dimension values
by: Cholaquidis, Alejandro, et al.
Published: (2024)

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
by: Kremer, Heiner, et al.
Published: (2022)

The phase diagram of kernel interpolation in large dimensions
by: Zhang, Haobo, et al.
Published: (2024)

High-accuracy and dimension-free sampling with diffusions
by: Gatmiry, Khashayar, et al.
Published: (2026)

Empirical Risk Minimization with Relative Entropy Regularization
by: Perlaza, Samir M., et al.
Published: (2022)

Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
by: Kungurtsev, Vyacheslav, et al.
Published: (2024)

Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution
by: Vardanyan, Elen, et al.
Published: (2023)

Agnostic Sample Compression Schemes for Regression
by: Attias, Idan, et al.
Published: (2018)

On the VC dimension of deep group convolutional neural networks
by: Sepliarskaia, Anna, et al.
Published: (2024)

How well behaved is finite dimensional Diffusion Maps?
by: Bo, Wenyu, et al.
Published: (2024)

Resolving Memorization in Empirical Diffusion Model for Manifold Data in High-Dimensional Spaces
by: Lyu, Yang, et al.
Published: (2025)

On the best approximation by finite Gaussian mixtures
by: Ma, Yun, et al.
Published: (2024)

The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
by: Bieringer, Sebastian, et al.
Published: (2023)

Empirical Likelihood for Random Forests and Ensembles
by: Chiang, Harold D., et al.
Published: (2025)

Empirical Error Estimates for Graph Sparsification
by: Wang, Siyao, et al.
Published: (2025)

Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression
by: Shi, Haoming, et al.
Published: (2026)

$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
by: Fujisawa, Masahiro, et al.
Published: (2025)

Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression
by: Hanchi, Ayoub El, et al.
Published: (2023)

Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rule
by: Lee, Joowon, et al.
Published: (2026)

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
by: Haas, Moritz, et al.
Published: (2023)

On Linear Separability under Linear Compression with Applications to Hard Support Vector Machine
by: McVay, Paul, et al.
Published: (2022)

Local minima of the empirical risk in high dimension: General theorems and convex examples
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)

A unified construction for series representations and finite approximations of completely random measures
by: Lee, Juho, et al.
Published: (2019)

Empirical Bayes Estimation for Lasso-Type Regularizers: Analysis of Automatic Relevance Determination
by: Yoshida, Tsukasa, et al.
Published: (2025)

The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli
by: Cohen, Doron, et al.
Published: (2024)

A distribution-free valid p-value for finite samples of bounded random variables
by: Alvarez, Joaquin
Published: (2024)

Risk Measures and Upper Probabilities: Coherence and Stratification
by: Fröhlich, Christian, et al.
Published: (2022)

Low-degree Lower bounds for clustering in moderate dimension
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)

Compression, Generalization and Learning
by: Campi, Marco C., et al.
Published: (2023)

Adaptive finite element type decomposition of Gaussian processes
by: Kim, Jaehoan, et al.
Published: (2025)

Compress Then Test: Powerful Kernel Testing in Near-linear Time
by: Domingo-Enrich, Carles, et al.
Published: (2023)

A variational approach to dimension-free self-normalized concentration
by: Chugg, Ben, et al.
Published: (2025)

Rapidly Varying Completely Random Measures for Modeling Extremely Sparse Networks
by: Kilian, Valentin, et al.
Published: (2025)

Corrected generalized cross-validation for finite ensembles of penalized estimators
by: Bellec, Pierre C., et al.
Published: (2023)