Active Learning for Decision-Making from Imbalanced Observational Data.
Iiris SundinPeter SchulamEero SiivolaAki VehtariSuchi SariaSamuel KaskiPublished in: ICML (2019)
Keyphrases
- active learning
- observational data
- decision making
- causal bayesian networks
- class imbalance
- experimental data
- imbalanced data classification
- causal discovery
- imbalanced class distribution
- decision makers
- directed acyclic graph
- semi supervised
- machine learning
- rare class
- causal effects
- causal models
- data mining
- causal relationships
- latent variables
- causal structure
- sample size
- training examples
- bayesian networks
- training set
- labeled data
- random sampling
- data sets
- decision trees
- upper bound
- information extraction
- imbalanced datasets
- unlabeled data
- class distribution
- influence diagrams