Scalable inference for Nonparametric Stochastic Approximation in Reproducing Kernel Hilbert Spaces

Meimei Liu, Zuofeng Shang, Yun Yang

Nonparametric Estimation of a Covariate-Adjusted Counterfactual Treatment Regimen Response Curve

Ashkan Ertefaie, Luke Duttweiler, Brent A. Johnson et al.

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data

Félix Camirand Lemyre, Raymond J. Carroll, Aurore Delaigle

Enhanced Feature Learning via Regularisation: Integrating Neural Networks and Kernel Methods

Bertille FOLLAIN, Francis BACH

We propose a new method for feature learning and function estimation in supervised learning via regularised empirical risk minimisation. Our approach ...

Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds

Paul Rosa, Judith Rousseau

We consider the nonparametric regression problem when the covariates are located on an unknown compact submanifold of a Euclidean space. Under definin...

Frontiers to the learning of nonparametric hidden Markov models

Kweku Abraham, Zacharie Naulet, Elisabeth Gassiat

Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric modelling of the ...

On the Ability of Deep Networks to Learn Symmetries from Data: A Neural Kernel Theory

Andrea Perin, Stephane Deny

Symmetries (transformations by group actions) are present in many datasets, and leveraging them holds considerable promise for improving predictions i...

Physics-informed Kernel Learning

Nathan Doumèche, Gérard Biau, Francis Bach et al.

Physics-informed machine learning typically integrates physical priors into the learning process by minimizing a loss function that includes both a da...

A Bayesian nonparametric approach to mediation and spillover effects with multiple mediators in cluster-randomized trials

Yuki Ohnishi, Fan Li

Multivariate Root-N-Consistent Smoothing Parameter Free Matching Estimators and Estimators of Inverse Density Weighted Expectations

Hajo Holzmann, Alexander Meister

Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift

Kaizheng Wang

Principal stratification with continuous post-treatment variables: nonparametric identification and semiparametric estimation

Sizhu Lu, others

Kernel Spectral Joint Embeddings for High-Dimensional Noisy Datasets using Duo-Landmark Integral Operators

Xiucai Ding, Rong Ma

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

Jianqing Fan, Jinhang Chai

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...

Efficient nonparametric estimators of discrimination measures with censored survival data

Marie Skov Breum, Torben Martinussen

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

Xueqin Wang, Zhanfeng Wang, Rui Pan et al.

Estimation and Inference for Nonparametric Expected Shortfall Regression over RKHS

Kean Ming Tan, Wen-Xin Zhou, Myeonghun Yu et al.