Iterative Teacher-Aware Learning
This work addresses the challenge of improving learning efficiency in machine teaching by modeling teacher-awareness, which is incremental as it extends cooperative pedagogy from discrete concept learning to parameter learning.
The paper tackles the problem of machine parameter learning by proposing an iterative teacher-aware learner that incorporates teacher intention into the likelihood function, achieving provably faster learning compared to naive algorithms, with validation on tasks like regression and classification using synthetic and real data.
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher's instruction mechanism, can infer the teacher's intention to learn faster. Recently, the benefits of integrating this cooperative pedagogy into machine concept learning in discrete spaces have been proved by multiple works. However, how cooperative pedagogy can facilitate machine parameter learning hasn't been thoroughly studied. In this paper, we propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function and learn provably faster compared with the naive learning algorithms used in previous machine teaching works. We give theoretical proof that the iterative teacher-aware learning (ITAL) process leads to local and global improvements. We then validate our algorithms with extensive experiments on various tasks including regression, classification, and inverse reinforcement learning using synthetic and real data. We also show the advantage of modeling teacher-awareness when agents are learning from human teachers.