Papers

Kernel density estimation with polyspherical data and its applications

Eduardo García-Portugués, Andrea Meilán-Vila

Nonparametric Test for Rough Volatility

Carsten H. Chong, Viktor Todorov

Kernel Meets Sieve: Transformed Hazards Models with Sparse Longitudinal Covariates

Dayu Sun, Zhuowei Sun, Xingqiu Zhao et al.

Analysis of Variance of Tensor Product Reproducing Kernel Hilbert Spaces on Metric Spaces

Zhanfeng Wang, Rui Pan, Xueqin Wang et al.

Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS

Myeonghun Yu, Yue Wang, Siyu Xie et al.

Online Estimation with Rolling Validation: Adaptive Nonparametric Estimation with Streaming Data

Tianyu Zhang, Jing Lei

A Geometrical Analysis of Kernel Ridge Regression and its Applications

Zong Shang, Guillaume Lecué, Georgios Gavrilopoulos

Improved Learning Theory for Kernel Distribution Regression with Two-Stage Sampling

François Bachoc, Louis Béthune, Alberto González-Sanz et al.

Symmetry: A General Structure in Nonparametric Regression

Louis Goldwater Christie, John A. D. Aston

Structured Matrix Learning under Arbitrary Entrywise Dependence and Estimation of Markov Transition Kernel

Jinhang Chai, Jianqing Fan

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed Learning Scheme for Data Silos

Shao-Bo Lin, Xiaotong Liu, Di Wang et al.

Data silos, mainly caused by privacy and interoperability, significantly constrain collaborations among different organizations with similar data for ...

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling

Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito et al.

In this work we consider the problem of numerical integration, i.e., approximating integrals with respect to a target probability measure using only p...

Variance-Aware Estimation of Kernel Mean Embedding

Geoffrey Wolfer, Pierre Alquier

An important feature of kernel mean embeddings (KME) is that the rate of convergence of the empirical KME to the true distribution KME can be bounded ...

Composite Goodness-of-fit Tests with Kernels

Oscar Key, Arthur Gretton, François-Xavier Briol et al.

We propose kernel-based hypothesis tests for the challenging composite testing problem, where we are interested in whether the data comes from any dis...

On the Approximation of Kernel functions

Paul Dommel, Alois Pichler

Various methods in statistical learning build on kernels considered in reproducing kernel Hilbert spaces. In applications, the kernel is often selecte...