Iterative Teacher-Aware Learning
- URL: http://arxiv.org/abs/2110.00137v1
- Date: Fri, 1 Oct 2021 00:27:47 GMT
- Title: Iterative Teacher-Aware Learning
- Authors: Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen,
Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
- Abstract summary: In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency.
We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
- Score: 136.05341445369265
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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