mlsanityData to 7 Oct 2026

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

arXiv 2302.08436 ↗First posted 16 Feb 2023stat.ML cs.LGcited by 11

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Abstract (arXiv)

We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based models within sequential decision-making loops, e.g. Gaussian processes from GPflow or GPflux, or neural networks from Keras. This modular mindset is central to the package and extends to our acquisition functions and the internal dynamics of the decision-making loop, both of which can be tailored and extended by researchers or engineers when tackling custom use cases. Trieste is a research-friendly and production-ready toolkit backed by a comprehensive test suite, extensive documentation, and available at https://github.com/secondmind-labs/trieste.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 16 Feb 2023.

Builds on

27 of 45 references.

References · 27

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  2. 2014Scalable Variational Gaussian Process Classificationcited by 226
  3. 2020Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimizationcited by 176
  4. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  5. 2016GPflow: A Gaussian process library using TensorFlowcited by 139
  6. 2015Batch Bayesian Optimization via Local Penalizationcited by 103
  7. 2021A survey on high-dimensional Gaussian process modeling with application to Bayesian optimizationcited by 76
  8. 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 66
  9. 2019Bayesian Optimisation over Multiple Continuous and Categorical Inputscited by 55
  10. 2020BOSS: Bayesian Optimization over String Spacescited by 54
  11. 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
  12. 2020Scalable Thompson Sampling using Sparse Gaussian Process Modelscited by 39
15 more
  1. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  2. 2021TREGO: a Trust-Region Framework for Efficient Global Optimizationcited by 28
  3. 2020Mercer Features for Efficient Combinatorial Bayesian Optimizationcited by 27
  4. 2021Emulation of physical processes with Emukitcited by 24
  5. 2020MUMBO: MUlti-task Max-value Bayesian Optimizationcited by 21
  6. 2014BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Banditscited by 18
  7. 2017GPflowOpt: A Bayesian Optimization Library using TensorFlowcited by 18
  8. 2020Bayesian Quantile and Expectile Optimisationcited by 17
  9. 2021GPflux: A Library for Deep Gaussian Processescited by 12
  10. 2021A portfolio approach to massively parallel Bayesian optimizationcited by 9
  11. 2022A Robust Multi-Objective Bayesian Optimization Framework Considering Input Uncertaintycited by 7
  12. 2022Fantasizing with Dual GPs in Bayesian Optimization and Active Learningcited by 7
  13. 2022Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisationcited by 5
  14. 2022A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designcited by 5
  15. 2022An Empirical Review of Model-based Adaptive Sampling for Global Optimization of Expensive Black-box Functionscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 2; 2026: 3.

11 papers.

  1. 2023Unexpected Improvements to Expected Improvement for Bayesian Optimizationcited by 144
  2. 2023SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processescited by 27
  3. 2024A survey and benchmark of high-dimensional Bayesian optimization of discrete sequencescited by 22
  4. 2024Stopping Bayesian Optimization with Probabilistic Regret Boundscited by 11
  5. 2024System-Aware Neural ODE Processes for Few-Shot Bayesian Optimizationcited by 3
  6. 2025Surrogate-based optimization of system architectures subject to hidden constraintscited by 3
  7. 2024No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraintscited by 1
  8. 2025On Some Tunable Multi-fidelity Bayesian Optimization Frameworkscited by 0
  9. 2026LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimizationcited by 0
  10. 2026B3O: Scalable Boltzmann Batch Bayesian Optimizationcited by 0
  11. 2026Expected Hypervolume Maximization for Multiobjective Optimization under Uncertaintiescited by 0