Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators

Matias D. Cattaneo, Yingjie Feng, Boris Shigida

Towards Unified Native Spaces in Kernel Methods

Xavier Emery, Emilio Porcu, Moreno Bevilacqua

There exists a plethora of parametric models for positive definite kernels in Euclidean spaces, and their use is ubiquitous in statistics, machine lea...

On the Robustness of Kernel Goodness-of-Fit Tests

François-Xavier Briol, Xing Liu

Goodness-of-fit testing is often criticized for its lack of practical relevance: since "all models are wrong", the null hypothesis that the data confo...

Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection

Nikita Zozoulenko, Thomas Cass, Lukas Gonon

The Mahalanobis distance is a classical tool used to measure the covariance-adjusted distance between points in $\mathbb{R}^d$. In this work, we exten...

Hierarchical and Stochastic Crystallization Learning: Geometrically Leveraged Nonparametric Regression with Delaunay Triangulation

Guosheng Yin, Jiaqi Gu

High-dimensionality is known to be the bottleneck for both nonparametric regression and the Delaunay triangulation. To efficiently exploit the advanta...

Universality of Kernel Random Matrices and Kernel Regression in the Quadratic Regime

Parthe Pandit, Zhichao Wang, Yizhe Zhu

Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much o...

Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions

Qian Lin, Haobo Zhang, Yicheng Li et al.

Motivated by studies of neural networks, particularly the neural tangent kernel theory, we investigate the large-dimensional behavior of kernel ridge ...

Biological Sequence Kernels with Guaranteed Flexibility

Alan N. Amin, Debora S. Marks, Eli N. Weinstein

Applying machine learning to biological sequences---DNA, RNA and protein---has enormous potential to advance human health and environmental sustainabi...

Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraintsGet access

Arnab Auddyand others

Identification and estimation of interaction effects in nonparametric additive regressionGet access

Seung Hyun Moonand others

Scalable inference for Nonparametric Stochastic Approximation in Reproducing Kernel Hilbert Spaces

Zuofeng Shang, Meimei Liu, 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

Scalable Bayesian inference for heat kernel Gaussian processes on manifoldsGet access

Junhui Heand others

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

Elisabeth Gassiat, Zacharie Naulet, Kweku Abraham

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

Gérard Biau, Nathan Doumèche, 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

Fan Li, Yuki Ohnishi

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

Torben Martinussen, Marie Skov Breum

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.