Transductive Active Learning with Application to Safe Bayesian Optimization

This repository accompanies the Safe BO application from the NeurIPS '24 paper "Transductive Active Learning: Theory and Applications".

The work was presented in an oral presentation at the ICML 2024 Workshop on Aligning Reinforcement Learning Experimentalists and Theorists. See the corresponding version of the paper here.

Getting started

Installation

Requires Python 3.11.

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

CI checks

• The code is auto-formatted using black ..
• Static type checks can be run using pyright.
• Tests can be run using python -m pytest tests.

Documentation

To start a local server hosting the documentation run PYTHONPATH=$(pwd) pdoc ./lib --math.

Reproducing the experiments

The examples directory contains python scripts that reproduce the experiments from the paper. These examples simultaneously serve as examples of how to use the library in the lib directory.