# Mind the Gap: Measuring Generalization Performance Across Multiple Objectives

arXiv: https://arxiv.org/abs/2212.04183 · HTML: https://mlsanity.com/paper/2212.04183/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution](https://mlsanity.com/paper/1804.09081/) (2018)
- [Bayesian Optimization with Unknown Constraints](https://mlsanity.com/paper/1403.5607/) (2014)
- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [OpenML-Python: an extensible Python API for OpenML](https://mlsanity.com/paper/1911.02490/) (2019)
- [Distributionally Robust Bayesian Optimization](https://mlsanity.com/paper/2002.09038/) (2020)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [A General Framework for Constrained Bayesian Optimization using Information-based Search](https://mlsanity.com/paper/1511.09422/) (2015)
- [Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview](https://mlsanity.com/paper/2206.07438/) (2022)
- [A survey on multi-objective hyperparameter optimization algorithms for Machine Learning](https://mlsanity.com/paper/2111.13755/) (2021)
- [Multi-objective Asynchronous Successive Halving](https://mlsanity.com/paper/2106.12639/) (2021)
- [Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability](https://mlsanity.com/paper/1904.03867/) (2019)
- [Software Engineering for Fairness: A Case Study with Hyperparameter Optimization](https://mlsanity.com/paper/1905.05786/) (2019)
- [Promoting Fairness through Hyperparameter Optimization](https://mlsanity.com/paper/2103.12715/) (2021)
- [Constrained Multi-Objective Optimization for Automated Machine Learning](https://mlsanity.com/paper/1908.04909/) (2019)
- [An Empirical Study of Modular Bias Mitigators and Ensembles](https://mlsanity.com/paper/2202.00751/) (2022)
- [Bi-objective Ranking and Selection Using Stochastic Kriging](https://mlsanity.com/paper/2209.03919/) (2022)

## Built on by (most cited first, 3 of 3)

- [Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML](https://mlsanity.com/paper/2303.08485/) (2023)
- [Risk-Controlling Model Selection via Guided Bayesian Optimization](https://mlsanity.com/paper/2312.01692/) (2023)
- [MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization](https://mlsanity.com/paper/2605.18869/) (2026)

## Abstract (arXiv)

Modern machine learning models are often constructed taking into account multiple objectives, e.g., minimizing inference time while also maximizing accuracy. Multi-objective hyperparameter optimization (MHPO) algorithms return such candidate models, and the approximation of the Pareto front is used to assess their performance. In practice, we also want to measure generalization when moving from the validation to the test set. However, some of the models might no longer be Pareto-optimal which makes it unclear how to quantify the performance of the MHPO method when evaluated on the test set. To resolve this, we provide a novel evaluation protocol that allows measuring the generalization performance of MHPO methods and studying its capabilities for comparing two optimization experiments.