Cover Image

Nyström Kernel Stein Discrepancy

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kalinke, Florian, Szabo, Zoltan, Sriperumbudur, Bharath K.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Statistics Theory 46E22 (Primary) 62G10 (Secondary) G.3; I.2.6
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Nyström Kernel Stein Discrepancy Tests
by: Kalinke, Florian, et al.
Published: (2026)

The Minimax Lower Bound of Kernel Stein Discrepancy Estimation
by: Cribeiro-Ramallo, Jose, et al.
Published: (2025)

The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels
by: Kalinke, Florian, et al.
Published: (2024)

Kernel Integrated $R^2$: A Measure of Dependence
by: Roudaki, Pouya, et al.
Published: (2026)

Optimal Online Change Detection via Random Fourier Features
by: Kalinke, Florian, et al.
Published: (2025)

Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy
by: Hagrass, Omar, et al.
Published: (2024)

Minimax Optimal Kernel Two-Sample Tests with Random Features
by: Mukherjee, Soumya, et al.
Published: (2025)

Spectral Regularized Kernel Two-Sample Tests
by: Hagrass, Omar, et al.
Published: (2022)

Spectral Regularized Kernel Goodness-of-Fit Tests
by: Hagrass, Omar, et al.
Published: (2023)

Nyström $M$-Hilbert-Schmidt Independence Criterion
by: Kalinke, Florian, et al.
Published: (2023)

Keep it Tighter -- A Story on Analytical Mean Embeddings
by: Chamakh, Linda, et al.
Published: (2021)

Adaptive Nonparametric Estimation via Kernel Transport on Group Orbits: Oracle Inequalities and Minimax Rates
by: Nembe, Jocelyn
Published: (2025)

A simplified directional KeRF algorithm
by: Isidoros, Iakovidis, et al.
Published: (2024)

The Pokémon Theorem and other Fairness Impossibility Results
by: Smola, Daniel Matsui, et al.
Published: (2026)

The Optimality of Kernel Classifiers in Sobolev Space
by: Lai, Jianfa, et al.
Published: (2024)

Kernel Embeddings and the Separation of Measure Phenomenon
by: Santoro, Leonardo V., et al.
Published: (2025)

Kernel-Based Nonparametric Tests For Shape Constraints
by: Sen, Rohan
Published: (2025)

OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data
by: Wette, Sebastian, et al.
Published: (2024)

Deep Networks are Reproducing Kernel Chains
by: Heeringa, Tjeerd Jan, et al.
Published: (2025)

Generalized Maximum Entropy: When and Why you need it
by: Ferro, Giuseppe M., et al.
Published: (2025)

Vector-Valued Reproducing Kernel Banach Spaces for Neural Networks and Operators
by: Dummer, Sven, et al.
Published: (2025)

Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecasting
by: Redhead, Benjamin R., et al.
Published: (2026)

Moment Expansions of the Energy Distance
by: Langmore, Ian
Published: (2025)

Nonparametric Linear Feature Learning in Regression Through Regularisation
by: Follain, Bertille, et al.
Published: (2023)

Kernel Embedding for Operator-Valued Measures and Its Application to Quantum Tomography
by: Mayer, Philipp Nikolas, et al.
Published: (2026)

Kernel-based independence and mean independence tests for weakly dependent data
by: Diz-Castro, Daniel, et al.
Published: (2026)

Partial correlation graphs for continuous-parameter time series
by: Fasen-Hartmann, Vicky, et al.
Published: (2024)

Kernel Single-Index Bandits: Estimation, Inference, and Learning
by: Arya, Sakshi, et al.
Published: (2026)

Random Field Representations of Kernel Distances
by: Langmore, Ian
Published: (2025)

Embeddings between Barron spaces with higher order activation functions
by: Heeringa, Tjeerd Jan, et al.
Published: (2023)

Mixed orthogonality graphs for continuous-time stationary processes
by: Fasen-Hartmann, Vicky, et al.
Published: (2023)

Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension
by: Singh, Rahul
Published: (2021)

Multivariate Spatio-Temporal Neural Hawkes Processes
by: Chukwuemeka, Christopher, et al.
Published: (2026)

Kernel $ε$-Greedy for Multi-Armed Bandits with Covariates
by: Arya, Sakshi, et al.
Published: (2023)

Efficient Learning of Stationary Diffusions with Stein-type Discrepancies
by: Bleile, Fabian, et al.
Published: (2026)

Data-Driven Discovery of Feature Groups in Clinical Time Series
by: Sergeev, Fedor, et al.
Published: (2025)

Exact Upper and Lower Bounds for the Output Distribution of Neural Networks with Random Inputs
by: Kofnov, Andrey, et al.
Published: (2025)

Testing parametric models for the angular measure for bivariate extremes
by: Lhaut, Stéphane, et al.
Published: (2024)

GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis
by: Heinrichs, Florian
Published: (2024)

Quantile Additive Trend Filtering
by: Zhang, Zhi, et al.
Published: (2023)