Targeted active learning for probabilistic models

Understand

A fundamental task in science is to design experiments that yield valuable insights about the system under study.

• Mathematically, these insights can be represented as a utility or risk function that shapes the value of conducting each experiment.
• We present PDBAL, a targeted active learning method that adaptively designs experiments to maximize scientific utility.
• PDBAL takes a user-specified risk function and combines it with a probabilistic model of the experimental outcomes to choose designs that rapidly converge on a high-utility model.