Kavli Affiliate: Francis J. Doyle
| First 5 Authors: Dinesh Krishnamoorthy, Francis J. Doyle III, , ,
| Summary:
This paper considers the problem of Bayesian optimization for systems with
safety-critical constraints, where both the objective function and the
constraints are unknown, but can be observed by querying the system. In
safety-critical applications, querying the system at an infeasible point can
have catastrophic consequences. Such systems require a safe learning framework,
such that the performance objective can be optimized while satisfying the
safety-critical constraints with high probability. In this paper we propose a
safe Bayesian optimization framework that ensures that the points queried are
always in the interior of the partially revealed safe region, thereby
guaranteeing constraint satisfaction with high probability. The proposed
interior-point Bayesian optimization framework can be used with any acquisition
function, making it broadly applicable. The performance of the proposed method
is demonstrated using a personalized insulin dosing application for patients
with type 1 diabetes.
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