Multi-Fidelity Bayesian Optimization With Across-Task Transferable Max-Value Entropy Search
In many applications, ranging from logistics to engineering, a designer is faced with a sequence of optimization tasks for which the objectives are in the form of black-box functions that are costly to evaluate. Furthermore, higher-fidelity evaluations of the optimization objectives often entail a larger cost. Existing multi-fidelity black-box optimization strategies select candidate solutions and fidelity levels with the goal of maximizing the information about the optimal value or the optimal solution for the current task. Assuming that successive optimization tasks are related, this paper introduces a novel information-theoretic acquisition function that balances the need to acquire information about the current task with the goal of collecting information transferable to future tasks. The proposed method transfers across tasks distributions over parameters of a Gaussian process surrogate model by implementing particle-based variational Bayesian updates. Theoretical insights based on the analysis of the expected regret substantiate the benefits of acquiring transferable knowledge across tasks. Furthermore, experimental results across synthetic and real-world examples reveal that the proposed acquisition strategy that caters to future tasks can significantly improve the optimization efficiency as soon as a sufficient number of tasks is processed.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach
Testing controllers in safety-critical systems is vital for ensuring their safety and preventing failures. In this paper, we address the falsification problem within learning-based closed-loop control systems through sim…
Bayesian OptimizationMUMBO: MUlti-task Max-value Bayesian Optimization
We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functio…
Bayesian OptimizationNon-Myopic Multifidelity Bayesian Optimization
Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelity representations of expensive objective…
Bayesian OptimizationMulti-Fidelity Bayesian Optimization via Deep Neural Networks
Bayesian optimization (BO) is a popular framework to optimize black-box functions. In many applications, the objective function can be evaluated at multiple fidelities to enable a trade-off between the cost and accuracy.…
Bayesian OptimizationCombining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, wh…
Bayesian Optimization