AI RESEARCH
Dynamic Hyperparameter Importance for Efficient Multi-Objective Optimization
arXiv CS.LG
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ArXi:2601.03166v2 Announce Type: replace Choosing a suitable ML model is a complex task that can depend on several objectives, e.g., accuracy, fairness, or energy consumption. In practice, this requires trading off multiple, often competing, objectives through multi-objective optimization (MOO). However, existing MOO methods typically treat all hyperparameters as equally important, disregarding that hyperparameter importance (HPI) can vary significantly across objectives.