In this repository can be found implementation of the aggregations and weighting approaches for products of Gaussian process experts proposed in our paper [1], along with several previously proposed PoEs.
- PoE
- gPoE
- BCM
- rBCM
- Barycenter
Along with other baselines:
- Full GP
- Linear regression
- Differential Entropy
- Softmax-Variance
- Uniform
- No-weights
Unzip the airline dataset Code/bayesian_benchmarks_modular/bayesian_benchmarks/data/airline/DelayedFlights_all.csv.zip
Please move into Code/bayesian_benchmarks_modular and run:
- python -m pytest bayesian_benchmarks/scripts/run_all_pytest.py -n X
where X is the number of experiments ran in parallel.
Results can be viewed in Code/bayesian_benchmarks_modular/bayesian_benchmarks/results/view_results.ipynb
- Tensorflow 2.0
- GPflow 2.0.1
- Numpy 0.18.1
- Pandas 0.25.1
- pytest-xdist
- tqdm 4.32.1
- sklearn 0.21.2
This repository complements our paper:
[1] Healing Products of Gaussian Process Experts, Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth, International Conference in Machine Learning 2020