Search Results for author:
Found 8 papers, 5 papers with code
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects
These improved estimators are inspired by doubly robust estimators of the causal mean, using a similar form within the kernel space.
Explaining Preferences with Shapley Values
While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored.
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI).
Giga-scale Kernel Matrix Vector Multiplication on GPU
Kernel matrix-vector multiplication (KMVM) is a foundational operation in machine learning and scientific computing.
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment
Causal inference grows increasingly complex as the number of confounders increases.
RKHS-SHAP: Shapley Values for Kernel Methods
Feature attribution for kernel methods is often heuristic and not individualised for each prediction.
Survival Regression with Proper Scoring Rules and Monotonic Neural Networks
We consider frequently used scoring rules for right-censored survival regression models such as time-dependent concordance, survival-CRPS, integrated Brier score and integrated binomial log-likelihood, and prove that neither of them is a proper scoring rule.
Large Scale Tensor Regression using Kernels and Variational Inference
We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakness.
