MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costs

Candelieri, Antonio; Archetti, Francesco

doi:10.48550/arxiv.2102.04951

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Ongoing and future works will focus on a larger set of experiments, including more ML algorithms and fairness datasets, and on investigating the possibility to also consider cost-aware optimization, recently proposed in (Lee et al, 2020;Candelieri & Archetti, 2021a;Luong et al, 2021), where sources' query costs are not fixed but depends on the hyperparameters configuration to evaluate. Although the two-steps acquisition function proposed in FanG-HPO should not require any awareness about location-dependent costs (i.e., after choosing x the query costs only depends on the size of dataset underlying the information sources), it could be anyway interesting to investigate this topic.…”

Section: Discussionmentioning

confidence: 99%

Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization

Candelieri¹,

Ponti²,

Archetti³

2022

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There is a consensus that focusing only on accuracy in searching for optimal machine learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for machine learning models which offer equally Paretoefficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware machine learning algorithms -which optimize accuracy constrained to some threshold on fairness -they could drastically increase the energy consumption in the case of large datasets. In this paper we propose FanG-HPO, a Fair and Green Hyperparameter Optimization (HPO) approach based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset (aka information sources) to obtain cheap approximations of both accuracy and fairness, and multi-objective Bayesian Optimization to efficiently identify Pareto-efficient machine learning models. Experiments consider two benchmark (fairness) datasets and two machine learning algorithms (XGBoost and Multi-Layer Perceptron), and provide an assessment of FanG-HPO against both fairness-aware machine learning algorithms and hyperparameter optimization via a multi-objective single-source optimization algorithm in BoTorch, a state-of-the-art platform for Bayesian Optimization.

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Section: Discussionmentioning

confidence: 99%