mlsanityData to 7 Oct 2026

Bayesian Optimization for Cascade-type Multi-stage Processes

arXiv 2111.08330 ↗First posted 16 Nov 2021stat.ML cs.LG math.OCcited by 17

Not on the map. Why?

Abstract (arXiv)

Complex processes in science and engineering are often formulated as multistage decision-making problems. In this paper, we consider a type of multistage decision-making process called a cascade process. A cascade process is a multistage process in which the output of one stage is used as an input for the subsequent stage. When the cost of each stage is expensive, it is difficult to search for the optimal controllable parameters for each stage exhaustively. To address this problem, we formulate the optimization of the cascade process as an extension of the Bayesian optimization framework and propose two types of acquisition functions based on credible intervals and expected improvement. We investigate the theoretical properties of the proposed acquisition functions and demonstrate their effectiveness through numerical experiments. In addition, we consider an extension called suspension setting in which we are allowed to suspend the cascade process at the middle of the multistage decision-making process that often arises in practical problems. We apply the proposed method in a test problem involving a solar cell simulator, which was the motivation for this study.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 12 of its 43 references that are in the map. First posted on arXiv 16 Nov 2021.

Builds on

12 of 43 references.

References · 12

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2015Batch Bayesian Optimization via Local Penalizationcited by 103
  4. 2018Information Directed Sampling and Bandits with Heteroscedastic Noisecited by 75
  5. 2019Multi-objective Bayesian Optimization using Pareto-frontier Entropycited by 57
  6. 2015GLASSES: Relieving The Myopia Of Bayesian Optimisationcited by 38
  7. 2019BINOCULARS for Efficient, Nonmyopic Sequential Experimental Designcited by 35
  8. 2021Sequential- and Parallel- Constrained Max-value Entropy Search via Information Lower Boundcited by 23
  9. 2019Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelizationcited by 19
  10. 2019Bayesian optimisation under uncertain inputscited by 18
  11. 2020Mean-Variance Analysis in Bayesian Optimization under Uncertaintycited by 18
  12. 2021Active learning for distributionally robust level-set estimationcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 4; 2024: 3; 2025: 3; 2026: 3.

17 papers.

Sort:

  1. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  2. 2022Model-based Causal Bayesian Optimizationcited by 26
  3. 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Boundscited by 16
  4. 2023Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Boundscited by 15
  5. 2023Bayesian Optimization of Function Networks with Partial Evaluationscited by 10
  6. 2023Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertaintycited by 5
  7. 2024Regret Analysis for Randomized Gaussian Process Upper Confidence Boundcited by 4
  8. 2022Regret Bounds for Noise-Free Cascaded Kernelized Banditscited by 3
  9. 2024Active Learning for Level Set Estimation Using Randomized Straddle Algorithmscited by 3
  10. 2025Distributionally Robust Active Learning for Gaussian Process Regressioncited by 3
  11. 2022Validation of Composite Systems by Discrepancy Propagationcited by 2
  12. 2026Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Boundscited by 2
5 more
  1. 2026On Regret Bounds of Thompson Sampling for Bayesian Optimizationcited by 2
  2. 2024Reducing Hyperparameter Tuning Costs in ML, Vision and Language Model Training Pipelines via Memoization-Awarenesscited by 0
  3. 2025Fast Bayesian Optimization of Function Networks with Partial Evaluationscited by 0
  4. 2025Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labscited by 0
  5. 2026Towards Regret Guarantees for One-Step Lookahead Bayesian Optimizationcited by 0