NNGP for active learning

This repository showcases application of NNGP approach for active learning. The approach is described in the paper Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning by Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev and Maxim Panov

Model task is 10D Rosenbrock function regression; we start from small training set and then sampling additional data from bigger data pool on each iteration. The goal is to evaluate which samples from the pool will speed-up the training by using uncertainty estimation.

We compare three approaches:

• NNGP (presented approach)
• MCDUE (common approach for uncertainty estimation)
• Random sampling

python al_rosenbrock_experiment.py

You can tweak some training parameters; to get the list of parameters, read the help

python al_rosenbrock_experiment.py --help