42 citations
- Centre National de la Recherche ScientifiqueFR4 papers
- Sorbonne UniversitéFR2 papers
- Carnegie Mellon UniversityUS1 paper
- Centre d'études et de recherche en informatique et communicationsFR1 paper
- Charles UniversityCZ1 paper
- Chinese University of Hong KongHK1 paper
- Corvinus University of BudapestHU1 paper
- Decision ResearchUS1 paper
- Decision Sciences (United States)US1 paper
- Decision Systems (United States)US1 paper
- Département d'InformatiqueFR1 paper
- École Normale Supérieure - PSLFR1 paper
4 papers · 1 filter
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten +6
The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires b…
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +2
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is hig…
MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts
Diederick Vermetten, Furong Ye, Thomas Bäck +1
Extending a recent suggestion to generate new instances for numerical black-box optimization benchmarking by interpolating pairs of the well-established BBOB functions from the COm…
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
Laurent Meunier, Herilalaina Rakotoarison, Pak Kan Wong +5
Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and…