971 citations · 3k across the 74 of their papers we have counts for
9 papers · 1 filter
Kernel Biclustering algorithm in Hilbert Spaces
Marcos Matabuena, J. C Vidal, Oscar Hernan Madrid Padilla +1
Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functi…
Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'
Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó +2
We discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based…
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Robert Hu, Dino Sejdinovic, Robin J. Evans
Causal inference grows increasingly complex as the number of confounders increases. Given treatments , confounders and outcomes , we develop a non-parametric method to te…
Bayesian Kernel Two-Sample Testing
Qinyi Zhang, Veit Wild, Sarah Filippi +2
In modern data analysis, nonparametric measures of discrepancies between random variables are particularly important. The subject is well-studied in the frequentist literature, whi…
Discussion of "Functional Models for Time-Varying Random Objects'' by Dubey and Müller
Dino Sejdinovic
The discussion focuses on metric covariance, a new association measure between paired random objects in a metric space, developed by Dubey and Müller, and on its relationship with…
A kernel log-rank test of independence for right-censored data
Tamara Fernandez, Arthur Gretton, David Rindt +1
We introduce a general non-parametric independence test between right-censored survival times and covariates, which may be multivariate. Our test statistic has a dual interpretatio…
Detecting causal associations in large nonlinear time series datasets
Jakob Runge, Peer Nowack, Marlene Kretschmer +2
Identifying causal relationships from observational time series data is a key problem in disciplines such as climate science or neuroscience, where experiments are often not possib…
A Kernel Test for Three-Variable Interactions
Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
We introduce kernel nonparametric tests for Lancaster three-variable interaction and for total independence, using embeddings of signed measures into a reproducing kernel Hilbert s…
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton +1
We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from t…