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Found 6 papers, 2 papers with code
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems.
PI is back! Switching Acquisition Functions in Bayesian Optimization
We also compare this to a random schedule and round-robin selection of EI and PI.
AutoML Adoption in ML Software
Machine learning (ML) has become essential to a vast range of applications, while ML experts are in short supply.
Identifying Properties of Real-World Optimisation Problems through a Questionnaire
Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences.
Adoption and Effects of Software Engineering Best Practices in Machine Learning
We conducted a survey among 313 practitioners to determine the degree of adoption for these practices and to validate their perceived effects.
Software Engineering
Towards Realistic Optimization Benchmarks: A Questionnaire on the Properties of Real-World Problems
This work aims to identify properties of real-world problems through a questionnaire on real-world single-, multi-, and many-objective optimization problems.
