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Found 5 papers, 3 papers with code
Additive interaction modelling using I-priors
Additive regression models with interactions are widely studied in the literature, using methods such as splines or Gaussian process regression.
Double Generative Adversarial Networks for Conditional Independence Testing
In this article, we study the problem of high-dimensional conditional independence testing, a key building block in statistics and machine learning.
Causal Modeling with Stochastic Confounders
This work extends causal inference with stochastic confounders.
Regression with I-priors
We describe in some detail the case that the regression function lies in the multidimensional fractional Brownian motion RKHS, when the I-prior methodology has some particular appeal.
Statistics Theory Statistics Theory
A Kernel Test for Three-Variable Interactions
We introduce kernel nonparametric tests for Lancaster three-variable interaction and for total independence, using embeddings of signed measures into a reproducing kernel Hilbert space.
