Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations

>> python3 run_regression.py --help
usage: run_regression.py [-h] [--num_inducing NUM_INDUCING]
                         [--minibatch_size MINIBATCH_SIZE]
                         [--iterations ITERATIONS] [--n_layers N_LAYERS]
                         --dataset DATASET [--fold FOLD]
                         [--prior_type {determinantal,normal,strauss,uniform}]
                         [--model {bsgp}]
                         [--num_posterior_samples NUM_POSTERIOR_SAMPLES]
                         [--step_size STEP_SIZE]

Run regression experiment

optional arguments:
  -h, --help            show this help message and exit
  --num_inducing NUM_INDUCING
  --minibatch_size MINIBATCH_SIZE
  --iterations ITERATIONS
  --n_layers N_LAYERS
  --dataset DATASET
  --fold FOLD
  --prior_type {determinantal,normal,strauss,uniform}
  --model {bsgp}
  --num_posterior_samples NUM_POSTERIOR_SAMPLES
  --step_size STEP_SIZE

To reproduce figures, plots and results make sure you have the correct requirements.

Ablation on priors for BSGP

Ablation on inference variables

Comparison of objectives

Training time

Reference

Rossi, S., Heinonen, M., Bonilla, E., Shen, Z. & Filippone, M.. (2021). Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:1837-1845

srossi93 / Sparse-Gaussian-Processes-Revisited