| Year | Title | Author | Publication | Code | Tasks | Notes | Datasets | Notions |
|---|---|---|---|---|---|---|---|---|
| 2021 | Global optimization based on active preference learning with radial basis functions | Bemporad and Piga | Machine Learning | - | multi-objective optimization |
expected improvement, Bayesian, None, Tra, Hard |
CIFAR-10 | aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning |
| 2021 | Toward optimal probabilistic active learning using a Bayesian approach | Kottke et al. | Machine Learning | - | Classification |
decision-theoretic selection strategy xPAL, 25 classifiers as the committee, None, Tra, Hard |
27 datasets from the openML library | directly optimizes the gain in misclassifica- tion error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to deal with uncertainties. |