mlsanityData to 7 Oct 2026

MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning

arXiv 2302.11533 ↗First posted 22 Feb 2023cs.LGcited by 5

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Abstract (arXiv)

In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating such black-box objective functions is often the effort required to prepare the system for measurement. We consider a common scenario where preparation costs grow as the distance between successive evaluations increases. In this setting, smooth optimisation trajectories are preferred and the jumpy paths produced by the standard myopic (i.e. one-step-optimal) Bayesian optimisation methods are sub-optimal. Our algorithm, MONGOOSE, uses a meta-learnt parametric policy to generate smooth optimisation trajectories, achieving performance gains over existing methods when optimising functions with large movement costs.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 33 of its 53 references that are in the map. First posted on arXiv 22 Feb 2023.

Builds on

33 of 53 references.

References · 33

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2015Scalable Bayesian Optimization Using Deep Neural Networkscited by 284
  3. 2021Learning to Optimize: A Primer and A Benchmarkcited by 206
  4. 2017Max-value Entropy Search for Efficient Bayesian Optimizationcited by 202
  5. 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functionscited by 201
  6. 2016COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Settingcited by 135
  7. 2018Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networkscited by 129
  8. 2020Efficiently Sampling Functions from Gaussian Process Posteriorscited by 125
  9. 2019Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimizationcited by 70
  10. 2022πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimizationcited by 69
  11. 2019Meta-learning of Sequential Strategiescited by 68
  12. 2022VeLO: Training Versatile Learned Optimizers by Scaling Upcited by 57
21 more
  1. 2022Constrained Efficient Global Optimization of Expensive Black-box Functionscited by 49
  2. 2021GIBBON: General-purpose Information-Based Bayesian OptimisatioNcited by 47
  3. 2022Discovering Evolution Strategies via Meta-Black-Box Optimizationcited by 44
  4. 2020Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Treescited by 41
  5. 2022GAUCHE: A Library for Gaussian Processes in Chemistrycited by 40
  6. 2020Scalable Thompson Sampling using Sparse Gaussian Process Modelscited by 39
  7. 2015GLASSES: Relieving The Myopia Of Bayesian Optimisationcited by 38
  8. 2020Meta-trained agents implement Bayes-optimal agentscited by 37
  9. 2019BINOCULARS for Efficient, Nonmyopic Sequential Experimental Designcited by 35
  10. 2023Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisationcited by 32
  11. 2020Mercer Features for Efficient Combinatorial Bayesian Optimizationcited by 27
  12. 2019Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rolloutcited by 23
  13. 2020MUMBO: MUlti-task Max-value Bayesian Optimizationcited by 21
  14. 2020Efficient Rollout Strategies for Bayesian Optimizationcited by 19
  15. 2020Bayesian Quantile and Expectile Optimisationcited by 17
  16. 2022SnAKe: Bayesian Optimization with Pathwise Explorationcited by 14
  17. 2021Kernel Identification Through Transformerscited by 13
  18. 2021Revisiting Bayesian Optimization in the light of the COCO benchmarkcited by 10
  19. 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systemscited by 7
  20. 2022Fantasizing with Dual GPs in Bayesian Optimization and Active Learningcited by 7
  21. 2022A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger designcited by 5

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 2.

5 papers.

  1. 2024Bayesian optimization as a flexible and efficient design framework for sustainable process systemscited by 15
  2. 2024Transition Constrained Bayesian Optimization via Markov Decision Processescited by 7
  3. 2025PABBO: Preferential Amortized Black-Box Optimizationcited by 7
  4. 2025In-Context Multi-Objective Optimizationcited by 5
  5. 2023Practical Path-based Bayesian Optimizationcited by 2