From a probabilistic standpoint, censored regression can be framed as the problem of recovering an unobservable, i.e. latent, function by only having access to ...
Sep 10, 2020 · We introduce a heteroscedastic multi-output Gaussian process model which combines the non-parametric flexibility of GPs with the ability to leverage ...
In this paper, we propose a het- eroscedastic multi-output censored Gaussian process model (HMOCGP) as a general approach to deal with vector-valued censored ...
This repository is the official implementation of the HMOCGP, from Generalized Multi-Output Gaussian Process Censored Regression. The full paper is available ...
May 4, 2022 · In this paper, we propose a het- eroscedastic multi-output censored Gaussian process model (HMOCGP) as a general approach to deal with vector- ...
Generalized multi-output Gaussian process censored regression
dl.acm.org › abs › j.patcog.2022.108751
Sep 1, 2022 · We empirically evaluate our model against other generative models for censored data on both synthetic and real world tasks and further show how ...
Implementation of Generalized Multi-Output Gaussian Process Censored Regression ------------------------------------------------------ This file contains ...
In this paper, as in the case of missing data, we argue that exploiting correlations between multiple outputs can enable models to better address the bias ...
In this paper, as in the case of missing data, we argue that exploiting correlations between multiple outputs can enable models to better address the bias ...
The variational sparse Gaussian process inducing variable framework and local variational methods are exploited to compute an analytically tractable lower ...