Explainable Bayesian Optimization
Manual parameter tuning of cyber-physical systems is a common practice, but it is labor-intensive. Bayesian Optimization (BO) offers an automated alternative, yet its black-box nature reduces trust and limits human-BO collaborative system tuning. Experts struggle to interpret BO recommendations due to the lack of explanations. This paper addresses the post-hoc BO explainability problem for cyber-physical systems. We introduce TNTRules (Tune-No-Tune Rules), a novel algorithm that provides both global and local explanations for BO recommendations. TNTRules generates actionable rules and visual graphs, identifying optimal solution bounds and ranges, as well as potential alternative solutions. Unlike existing explainable AI (XAI) methods, TNTRules is tailored specifically for BO, by encoding uncertainty via a variance pruning technique and hierarchical agglomerative clustering. A multi-objective optimization approach allows maximizing explanation quality. We evaluate TNTRules using established XAI metrics (Correctness, Completeness, and Compactness) and compare it against adapted baseline methods. The results demonstrate that TNTRules generates high-fidelity, compact, and complete explanations, significantly outperforming three baselines on 5 multi-objective testing functions and 2 hyperparameter tuning problems.
Code (1)
Tasks
Bayesian OptimizationHyperparameter OptimizationMultiobjective OptimizationSimilar Papers 제목 키워드 기반
Looping in the Human Collaborative and Explainable Bayesian Optimization
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specifie…
Bayesian OptimizationGaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds
Many functions have approximately-known upper and/or lower bounds, potentially aiding the modeling of such functions. In this paper, we introduce Gaussian process models for functions where such bounds are (approximately…
Bayesian OptimizationExplainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
Causal inference has recently gained notable attention across various fields like biology, healthcare, and environmental science, especially within explainable artificial intelligence (xAI) systems, for uncovering the ca…
Causal InferenceExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Federated LearningExplainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential for great gains in radiologist productivit…
Diagnosticimage-classificationImage ClassificationData-Driven Learning of 3-Point Correlation Functions as Microstructure Representations
This paper considers the open challenge of identifying complete, concise, and explainable quantitative microstructure representations for disordered heterogeneous material systems. Completeness and conciseness have been …
Bayesian Optimization