Differences
This shows you the differences between two versions of the page.
| — |
iterative_teaching [2017/05/31 19:56] (current) |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| + | https://arxiv.org/pdf/1705.10470v1.pdf Iterative Machine Teaching | ||
| + | |||
| + | In this paper, we consider the problem of machine | ||
| + | teaching, the inverse problem of machine | ||
| + | learning. Different from traditional machine | ||
| + | teaching which views the learners as batch algorithms, | ||
| + | we study a new paradigm where the | ||
| + | learner uses an iterative algorithm and a teacher | ||
| + | can feed examples sequentially and intelligently | ||
| + | based on the current performance of the learner. | ||
| + | We show that the teaching complexity in the iterative | ||
| + | case is very different from that in the batch | ||
| + | case. Instead of constructing a minimal training | ||
| + | set for learners, our iterative machine teaching | ||
| + | focuses on achieving fast convergence in the | ||
| + | learner model. Depending on the level of information | ||
| + | the teacher has from the learner model, | ||
| + | we design teaching algorithms which can provably | ||
| + | reduce the number of teaching examples and | ||
| + | achieve faster convergence than learning without | ||
| + | teachers. We also validate our theoretical findings | ||
| + | with extensive experiments on different data | ||
| + | distribution and real image datasets. | ||
