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October 1, 20240 citations

Pareto Powered Sampling Enhances Bayesian Optimization for Efficient Sampling

Pareto Powered Sampling: A Fresh Perspective on Sampling in Single Objective Batch Bayesian Optimization

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Authors

NNNazanin NezamiHAHadis Anahideh

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Overview

Proposed Pareto Powered Sampling improves convergence speed in single objective optimization tasks, indicating its robustness.

Key Points

  • Pareto Powered Sampling shows improved convergence speed and quality in optimization tasks; experiments validate this.
  • The method achieves better results compared to traditional bayesian optimization approaches like standard acquisition functions.
  • Using an innovative approach, the study addresses challenges with selecting from the Pareto front in batch settings.
  • This method highlights the importance of maintaining diversity in pareto-optimal solutions for robust optimization.
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Cite This Study

Nezami et al. (2024) studied this question.

synapsesocial.com/papers/68af6595ad7bf08b1eae5201https://doi.org/10.21872/2024iise_8134
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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  3. 3Multi-Objective Batch Energy-Entropy Acquisition Function for Bayesian Optimization2025 · 2 citations
  4. 4Penalization method to convert Bayesian optimization methods into batch multi-objective Bayesian optimization methods2026
  5. 5PABBO: Preferential Amortized Black-Box Optimization2025