Safe Exploration for Interactive Machine Learning

Understand

In Interactive Machine Learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function.

• While IML methods, e.g., Bayesian optimization and active learning, have been successful in applications, on real-world systems they must provably avoid unsafe decisions.
• To this end, safe IML algorithms must carefully learn about a priori unknown constraints without making unsafe decisions.
• Existing algorithms for this problem learn about the safety of all decisions to ensure convergence.