6 citations · 8 across the 7 of their papers we have counts for
5 papers · 1 filter
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects
Jake Fawkes, Robert Hu, Robin J. Evans +1
With the widespread application of causal inference, it is increasingly important to have tools which can test for the presence of causal effects in a diverse array of circumstance…
Explaining Preferences with Shapley Values
Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas +1
While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propos…
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Veit D. Wild, Robert Hu, Dino Sejdinovic
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). G…
Survival Regression with Proper Scoring Rules and Monotonic Neural Networks
David Rindt, Robert Hu, David Steinsaltz +1
We consider frequently used scoring rules for right-censored survival regression models such as time-dependent concordance, survival-CRPS, integrated Brier score and integrated bin…
Large Scale Tensor Regression using Kernels and Variational Inference
Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
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 weakn…