mlsanityData to 7 Oct 2026

Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

arXiv 1406.2541 ↗First posted 10 Jun 2014stat.ML cs.LGcited by 201

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Abstract (arXiv)

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.

Key facts. Cited by 201 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2014.

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Citing papers per year: 2021: 38; 2022: 36; 2023: 34; 2024: 37; 2025: 28; 2026: 28.

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  3. 2021Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvementcited by 115
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  10. 2022Pareto Set Learning for Expensive Multi-Objective Optimizationcited by 51
  11. 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
  12. 2022Joint Entropy Search for Multi-objective Bayesian Optimizationcited by 45
38 more
  1. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  2. 2021Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mutual Informationcited by 33
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  4. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  5. 2023Sampling from Gaussian Process Posteriors using Stochastic Gradient Descentcited by 27
  6. 2021BORE: Bayesian Optimization by Density-Ratio Estimationcited by 26
  7. 2022Thinking inside the box: A tutorial on grey-box Bayesian optimizationcited by 26
  8. 2022Spiking Graph Convolutional Networkscited by 26
  9. 2021Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Boundcited by 23
  10. 2023qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimizationcited by 23
  11. 2021Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimizationcited by 22
  12. 2021Multi-objective Asynchronous Successive Halvingcited by 22
  13. 2023Self-Correcting Bayesian Optimization through Bayesian Active Learningcited by 22
  14. 2023A General Framework for User-Guided Bayesian Optimizationcited by 22
  15. 2024Amortized Probabilistic Conditioning for Optimization, Simulation and Inferencecited by 22
  16. 2022Multi-Fidelity Cost-Aware Bayesian Optimizationcited by 20
  17. 2022A General Recipe for Likelihood-free Bayesian Optimizationcited by 19
  18. 2023Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processescited by 19
  19. 2021A Nonmyopic Approach to Cost-Constrained Bayesian Optimizationcited by 18
  20. 2021Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimizationcited by 18
  21. 2024MallowsPO: Fine-Tune Your LLM with Preference Dispersionscited by 18
  22. 2022Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimizationcited by 16
  23. 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Boundscited by 16
  24. 2023Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AIcited by 16
  25. 2024Cost-aware Bayesian Optimization via the Pandora's Box Gittins Indexcited by 16
  26. 2023Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Boundscited by 15
  27. 2022Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimizationcited by 14
  28. 2023BOtied: Multi-objective Bayesian optimization with tied multivariate rankscited by 14
  29. 2021An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemblecited by 13
  30. 2021Output Space Entropy Search Framework for Multi-Objective Bayesian Optimizationcited by 13
  31. 2023Multi-Objective Bayesian Optimization with Active Preference Learningcited by 13
  32. 2024Pareto Front-Diverse Batch Multi-Objective Bayesian Optimizationcited by 13
  33. 2021Lenient Regret and Good-Action Identification in Gaussian Process Banditscited by 12
  34. 2021Targeted Active Learning for Bayesian Decision-Makingcited by 12
  35. 2022A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functionscited by 12
  36. 2022Generalizing Bayesian Optimization with Decision-theoretic Entropiescited by 11
  37. 2023qPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson samplingcited by 11
  38. 2024Stopping Bayesian Optimization with Probabilistic Regret Boundscited by 11

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