mlsanityData to 14 Oct 2026

GPflowOpt: A Bayesian Optimization Library using TensorFlow

arXiv 1711.03845 ↗First posted 10 Nov 2017stat.MLcited by 18

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Abstract (arXiv)

A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlow including automatic differentiation, parallelization and GPU computations for Bayesian optimization. Design goals focus on a framework that is easy to extend with custom acquisition functions and models. The framework is thoroughly tested and well documented, and provides scalability. The current released version of GPflowOpt includes some standard single-objective acquisition functions, the state-of-the-art max-value entropy search, as well as a Bayesian multi-objective approach. Finally, it permits easy use of custom modeling strategies implemented in GPflow.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2017.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 6; 2024: 0; 2025: 2; 2026: 1.

18 papers.

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  1. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 148
  2. 2021OpenBox: A Generalized Black-box Optimization Servicecited by 46
  3. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  4. 2021Emulation of physical processes with Emukitcited by 24
  5. 2021Conditioning Sparse Variational Gaussian Processes for Online Decision-makingcited by 23
  6. 2023Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimizationcited by 16
  7. 2022BaCO: A Fast and Portable Bayesian Compiler Optimization Frameworkcited by 11
  8. 2023Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlowcited by 11
  9. 2021Revisiting Bayesian Optimization in the light of the COCO benchmarkcited by 10
  10. 2023OpenBox: A Python Toolkit for Generalized Black-box Optimizationcited by 9
  11. 2022PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimizationcited by 5
  12. 2023Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimizationcited by 5
6 more
  1. 2022Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Designcited by 4
  2. 2023PyBADS: Fast and robust black-box optimization in Pythoncited by 3
  3. 2023AutoML in Heavily Constrained Applicationscited by 3
  4. 2025Deep Reinforcement Learning-Based DRAM Equalizer Parameter Optimization Using Latent Representationscited by 1
  5. 2025Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimizationcited by 0
  6. 2026Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalizationcited by 0