What is it about?
This paper introduces a new method for improving the performance of AI and ML surrogate models. Surrogate models are like stand-ins for expensive simulations, and typically, one is chosen based on past experience or by comparing their errors. This paper presents a fresh approach by looking at the problem as a financial one, similar to managing an investment portfolio. In simple terms, the authors treat the errors made by different surrogate models as random variables. We use a clustering algorithm to select a subset of the best-performing models and calculate their weights, similar to how you would manage assets in a financial portfolio. This new approach significantly enhances the overall performance of the ensemble of models. To test our approach, we trained a large number of surrogate models and found that the clustering algorithm was able to select a smaller group of high-performing models, reducing the complexity while preserving diversity. Moreover, we demonstrated that the portfolio management approach led to better results for various input scenarios. In essence, this paper presents a way to improve the accuracy of surrogate models by borrowing ideas from finance. We show that this novel approach can be a valuable tool for engineers and researchers working with complex simulations.
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Why is it important?
The work is important as it introduces an innovative approach to enhance the efficiency of ML & AI predictive tasks through surrogate models. By likening model selection to financial portfolio management, it offers a systematic, data-driven method for choosing the best models, reducing computational costs while maintaining diversity. This cross-disciplinary perspective not only speeds up the design and optimization processes but also has broader implications for problem-solving in various domains. The practical case study demonstrates its real-world applicability, making it a valuable tool for researchers and engineers.
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This page is a summary of: HPOSS: A hierarchical portfolio optimization stacking strategy to reduce the generalization error of ensembles of models, PLoS ONE, August 2023, PLOS, DOI: 10.1371/journal.pone.0290331.
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