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Adjustable hyperparameters of machine learning models typically impactvarious key trade-offs such as accuracy, fairness, robustness, or inferencecost. Our goal in this paper is to find a configuration that adheres touser-specified limits on certain risks while being useful with respect to otherconflicting metrics. We solve this by combining Bayesian Optimization (BO) withrigorous risk-controlling procedures, where our core idea is to steer BOtowards an efficient testing strategy. Our BO method identifies a set of Paretooptimal configurations residing in a designated region of interest. Theresulting candidates are statistically verified and the best-performingconfiguration is selected with guaranteed risk levels. We demonstrate theeffectiveness of our approach on a range of tasks with multiple desiderata,including low error rates, equitable predictions, handling spuriouscorrelations, managing rate and distortion in generative models, and reducingcomputational costs.

Risk-Controlling Model Selection via Guided Bayesian Optimization