mlsanityData to 14 Oct 2026

Batch Bayesian Optimization for Replicable Experimental Design

arXiv 2311.01195 ↗First posted 2 Nov 2023cs.LG cs.AIcited by 8

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Abstract (arXiv)

Many real-world experimental design problems (a) evaluate multiple experimental conditions in parallel and (b) replicate each condition multiple times due to large and heteroscedastic observation noise. Given a fixed total budget, this naturally induces a trade-off between evaluating more unique conditions while replicating each of them fewer times vs. evaluating fewer unique conditions and replicating each more times. Moreover, in these problems, practitioners may be risk-averse and hence prefer an input with both good average performance and small variability. To tackle both challenges, we propose the Batch Thompson Sampling for Replicable Experimental Design (BTS-RED) framework, which encompasses three algorithms. Our BTS-RED-Known and BTS-RED-Unknown algorithms, for, respectively, known and unknown noise variance, choose the number of replications adaptively rather than deterministically such that an input with a larger noise variance is replicated more times. As a result, despite the noise heteroscedasticity, both algorithms enjoy a theoretical guarantee and are asymptotically no-regret. Our Mean-Var-BTS-RED algorithm aims at risk-averse optimization and is also asymptotically no-regret. We also show the effectiveness of our algorithms in two practical real-world applications: precision agriculture and AutoML.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 30 of its 45 references that are in the map. First posted on arXiv 2 Nov 2023.

Builds on

30 of 45 references.

References · 30

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18 more
  1. 2019No-Regret Bayesian Optimization with Unknown Hyperparameterscited by 51
  2. 2013Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Explorationcited by 50
  3. 2020Bayesian Optimization of Risk Measurescited by 36
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  7. 2022Sample-Then-Optimize Batch Neural Thompson Samplingcited by 25
  8. 2021Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processescited by 24
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Cited by year (2026 to 14 Oct)

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

8 papers.

  1. 2025Quantum Lipschitz Banditscited by 4
  2. 2024A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Liftingcited by 3
  3. 2025PIED: Physics-Informed Experimental Design for Inverse Problemscited by 2
  4. 2024Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimizationcited by 1
  5. 2025A Guide to Bayesian Optimization in Bioprocess Engineeringcited by 1
  6. 2025Weighted Euclidean Distance Matrices over Mixed Continuous and Categorical Inputs for Gaussian Process Modelscited by 0
  7. 2026Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRLcited by 0
  8. 2026Principled Design of Expected Improvement for Continuous Simulation Optimizationcited by 0