Generalized Multi-Output Gaussian Process Censored Regression

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This repository is the official implementation of the HMOCGP, from Generalized Multi-Output Gaussian Process Censored Regression.

The full paper is available here

Summary

This repository contains:

1. model.py: code to implement both homoscedastic and heteroscedastic versions of variational GPs
2. likelihoods.py, distributions.py: code to implement different likelihoods used in Section 3 of the paper (i.e., censored-Gaussian/Poisson/NegBinomial)
3. /data: folder containing data used for the New York Dataset experiment (Section 3)

Training and Evaluation code

A working Jupyter Notebook is provided in demo-nyc-bike-station3386-1h.ipynb, replicating results for the NYC task (more details in Section 3 of the paper).

The notebook contains:

1. Data Loading & Pre-processing
2. Training & Evaluation code for the NYC Experiment showcasing usage of three different likelihood functions (Gaussian, Poisson and NegBinomial as in Section 3)

Summary of results

In our work, we show how the proposed model is able to achieve better performance in capturing the latent non-censored process on a variety of different tasks. Below is a summary of the presented results: