Machine Teaching for Bayesian Learners in the Exponential Family
This work addresses the challenge of efficient machine teaching for Bayesian models, which is incremental as it builds on existing teaching frameworks but focuses on a specific learner type.
The paper tackles the problem of designing optimal training data for Bayesian learners by proposing a teaching framework that balances learner loss and teacher effort, and presents an approximate algorithm for conjugate exponential family models to find optimal teaching sets.
What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner? We propose an optimal teaching framework aimed at learners who employ Bayesian models. Our framework is expressed as an optimization problem over teaching examples that balance the future loss of the learner and the effort of the teacher. This optimization problem is in general hard. In the case where the learner employs conjugate exponential family models, we present an approximate algorithm for finding the optimal teaching set. Our algorithm optimizes the aggregate sufficient statistics, then unpacks them into actual teaching examples. We give several examples to illustrate our framework.