However, differential privacy's worst-case nature entails scaling such noise to the range of the queries even for

In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teachingfocuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner'snew state.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The result is aO(D T) regret bound that holds uniformly for all comparatorsu X.

The result is aO(D T) regret bound that holds uniformly for all comparatorsu X.