mlsanityData to 7 Oct 2026

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

arXiv 2406.04739 ↗First posted 7 Jun 2024cs.LG stat.MLcited by 22

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Abstract (arXiv)

Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal candidate for these tasks. Several methods for high-dimensional continuous and categorical Bayesian optimization have been proposed recently. However, our survey of the field reveals highly heterogeneous experimental set-ups across methods and technical barriers for the replicability and application of published algorithms to real-world tasks. To address these issues, we develop a unified framework to test a vast array of high-dimensional Bayesian optimization methods and a collection of standardized black-box functions representing real-world application domains in chemistry and biology. These two components of the benchmark are each supported by flexible, scalable, and easily extendable software libraries (poli and poli-baselines), allowing practitioners to readily incorporate new optimization objectives or discrete optimizers. Project website: https://machinelearninglifescience.github.io/hdbo_benchmark

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 95 of its 157 references that are in the map. First posted on arXiv 7 Jun 2024.

Builds on

95 of 157 references.

References · 95

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  2. 2018Junction Tree Variational Autoencoder for Molecular Graph Generationcited by 647
  3. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
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  11. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
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38 more
  1. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
  2. 2020Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retrainingcited by 121
  3. 2017Batched Large-scale Bayesian Optimization in High-dimensional Spacescited by 116
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  5. 2020Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimizationcited by 89
  6. 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
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Citing papers per year: 2024: 1; 2025: 11; 2026: 10.

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  2. 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spacescited by 13
  3. 2024Variational Search Distributionscited by 6
  4. 2025CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningcited by 2
  5. 2025Fine-tuning LLMs with variational Bayesian last layer for high-dimensional Bayesian optimizationcited by 2
  6. 2025Informed Initialization for Bayesian Optimization and Active Learningcited by 2
  7. 2025Generative Bayesian Optimization: Generative Models as Acquisition Functionscited by 2
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  10. 2026Time-Aware Latent Space Bayesian Optimizationcited by 1
  11. 2025Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimizationcited by 0
  12. 2025BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimizationcited by 0
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  3. 2026MolLIBRA: Genetic Molecular Optimization with Multi-Fingerprint Surrogates and Text-Molecule Aligned Criticcited by 0
  4. 2026Information Theoretic Bayesian Optimization over the Probability Simplexcited by 0
  5. 2026AutoScout: Structured Optimization for Automating ML System Configurationcited by 0
  6. 2026BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimizationcited by 0
  7. 2026Constraint-Aware Discrete Black-Box Optimization Using Tensor Decompositioncited by 0
  8. 2026Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisationcited by 0
  9. 2026Agentic High-Dimensional Bayesian Optimization with Hypothesis- and Evidence-Guided Searchcited by 0
  10. 2026Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineeringcited by 0