Bayesian Active Meta-Learning for Black-Box Optimization

2021-10-19 · Ivana Nikoloska, Osvaldo Simeone

Data-efficient learning algorithms are essential in many practical applications for which data collection is expensive, e.g., for the optimal deployment of wireless systems in unknown propagation scenarios. Meta-learning can address this problem by leveraging data from a set of related learning tasks, e.g., from similar deployment settings. In practice, one may have available only unlabeled data sets from the related tasks, requiring a costly labeling procedure to be carried out before use in meta-learning. For instance, one may know the possible positions of base stations in a given area, but not the performance indicators achievable with each deployment. To decrease the number of labeling steps required for meta-learning, this paper introduces an information-theoretic active task selection mechanism, and evaluates an instantiation of the approach for Bayesian optimization of black-box models.

📄 PDF Abstract BibTeX arXiv:2110.09943

Code (0)

등록된 구현이 없습니다.

Tasks

Bayesian OptimizationMeta-Learning

Methods 이 논문이 사용한 방법론

Gaussian Process Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for…

BayMOTH: Bayesian optiMizatiOn with meTa-lookahead -- a simple approacH

2026-04-13 · Rahman Ejaz, Varchas Gopalaswamy, Ricardo Luna, Aarne Lees 외 arxiv

Bayesian optimization (BO) has for sequential optimization of expensive black-box functions demonstrated practicality and effectiveness in many real-world settings. Meta-Bayesian optimization (meta-BO) focuses on improvi…

Modelling Human Active Search in Optimizing Black-box Functions

2020-03-09 · Antonio Candelieri, Riccardo Perego, Ilaria Giordani, Andrea Ponti 외

Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available…

Active Learning Bayesian Optimization Gaussian Processes

SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization

2026-01-29 · Leonard Papenmeier, Petru Tighineanu arxiv

Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the budget of evaluations. In many applications, historical data from related o…

Machine Learning Algorithms for Improving Black Box Optimization Solvers

2025-09-29 · Morteza Kimiaei, Vyacheslav Kungurtsev arxiv

Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods -- line search, direct search, and mo…

Reinforcement Learning

Resource Aware Multifidelity Active Learning for Efficient Optimization

2020-07-09 · Francesco Grassi, Giorgio Manganini, Michele Garraffa, Laura Mainini

Traditional methods for black box optimization require a considerable number of evaluations which can be time consuming, unpractical, and often unfeasible for many engineering applications that rely on accurate represent…

Active Learning Bayesian Optimization Distributed Computing