Cover Image

Regularized $f$-Divergence Kernel Tests

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ribero, Mónica, Schrab, Antonin, Gretton, Arthur
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Practical Kernel Tests of Conditional Independence
by: Pogodin, Roman, et al.
Published: (2024)

A Unified View of Optimal Kernel Hypothesis Testing
by: Schrab, Antonin
Published: (2025)

A Practical Introduction to Kernel Discrepancies: MMD, HSIC & KSD
by: Schrab, Antonin
Published: (2025)

Robust Kernel Hypothesis Testing under Data Corruption
by: Schrab, Antonin, et al.
Published: (2024)

Credal Two-Sample Tests of Epistemic Uncertainty
by: Chau, Siu Lun, et al.
Published: (2024)

Differentially Private Permutation Tests: Applications to Kernel Methods
by: Kim, Ilmun, et al.
Published: (2023)

Kernel Single Proxy Control for Deterministic Confounding
by: Xu, Liyuan, et al.
Published: (2023)

DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing
by: Zhou, Zhijian, et al.
Published: (2025)

Fast and Scalable Score-Based Kernel Calibration Tests
by: Glaser, Pierre, et al.
Published: (2025)

Composite Goodness-of-fit Tests with Kernels
by: Key, Oscar, et al.
Published: (2021)

Near-Optimality of Contrastive Divergence Algorithms
by: Glaser, Pierre, et al.
Published: (2025)

Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
by: Zenati, Houssam, et al.
Published: (2026)

Kernel Treatment Effects with Adaptively Collected Data
by: Zenati, Houssam, et al.
Published: (2025)

Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
by: Singh, Rahul, et al.
Published: (2021)

Controlling Moments with Kernel Stein Discrepancies
by: Kanagawa, Heishiro, et al.
Published: (2022)

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
by: Wornbard, Jakub, et al.
Published: (2026)

Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
by: Meunier, Dimitri, et al.
Published: (2024)

Sobolev Regularized MMD Gradient Flow
by: Tian, Chenyang, et al.
Published: (2026)

Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm
by: Li, Zhu, et al.
Published: (2023)

Perturbative methods for non-parametric instrumental variable
by: Bu, Wei, et al.
Published: (2026)

Empirical Risk Minimization with $f$-Divergence Regularization
by: Daunas, Francisco, et al.
Published: (2026)

Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
by: Meunier, Dimitri, et al.
Published: (2024)

Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
by: Zenati, Houssam, et al.
Published: (2025)

Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation
by: Xu, Liyuan, et al.
Published: (2021)

Regularization via f-Divergence: An Application to Multi-Oxide Spectroscopic Analysis
by: Li, Weizhi, et al.
Published: (2025)

Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
by: Daunas, Francisco, et al.
Published: (2024)

Interventional Processes for Causal Uncertainty Quantification
by: Dance, Hugh, et al.
Published: (2024)

A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
by: Daunas, Francisco, et al.
Published: (2025)

A Unified Data Representation Learning for Non-parametric Two-sample Testing
by: Tian, Xunye, et al.
Published: (2024)

On the Hardness of Conditional Independence Testing In Practice
by: He, Zheng, et al.
Published: (2025)

Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization
by: Tang, Cheng, et al.
Published: (2024)

Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
by: Chen, Zonghao, et al.
Published: (2025)

Regularized least squares learning with heavy-tailed noise is minimax optimal
by: Mollenhauer, Mattes, et al.
Published: (2025)

Deep MMD Gradient Flow without adversarial training
by: Galashov, Alexandre, et al.
Published: (2024)

$f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses
by: Wu, Di, et al.
Published: (2026)

Accelerated Diffusion Models via Speculative Sampling
by: De Bortoli, Valentin, et al.
Published: (2025)

Nonlinear Meta-Learning Can Guarantee Faster Rates
by: Meunier, Dimitri, et al.
Published: (2023)

Foundations of Multivariate Distributional Reinforcement Learning
by: Wiltzer, Harley, et al.
Published: (2024)

On Weak-to-Strong Generalization and f-Divergence
by: Yao, Wei, et al.
Published: (2025)

Differentially Private Optimization for Non-Decomposable Objective Functions
by: Kong, Weiwei, et al.
Published: (2023)