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Machine learning (ML) methods offer a wide range of configurablehyperparameters that have a significant influence on their performance. Whileaccuracy is a commonly used performance objective, in many settings, it is notsufficient. Optimizing the ML models with respect to multiple objectives suchas accuracy, confidence, fairness, calibration, privacy, latency, and memoryconsumption is becoming crucial. To that end, hyperparameter optimization, theapproach to systematically optimize the hyperparameters, which is alreadychallenging for a single objective, is even more challenging for multipleobjectives. In addition, the differences in objective scales, the failures, andthe presence of outlier values in objectives make the problem even harder. Wepropose a multi-objective Bayesian optimization (MoBO) algorithm that addressesthese problems through uniform objective normalization and randomized weightsin scalarization. We increase the efficiency of our approach by imposingconstraints on the objective to avoid exploring unnecessary configurations(e.g., insufficient accuracy). Finally, we leverage an approach to parallelizethe MoBO which results in a 5x speed-up when using 16x more workers.

Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives