近最优教学人群分类
Near-Optimally Teaching the Crowd to Classify
摘要：
我们应该如何向学习者提供训练样本来教授分类规则？这是培训工人进行众包标签任务时的一个自然问题，也是由数据驱动的在线教育中的挑战激发的。我们提出了一个自然的随机模型的学习者，建模为随机切换假设的基础上观察到的反馈。然后，我们开发STRICT，一个有效的算法，选择的例子教工人。我们的解决方案greetly最大化的子模块代理目标函数，以选择的例子，以显示给学习者。我们证明了我们的策略与最优教学策略是有竞争力的。此外，对于线性分离器的特殊情况下，我们证明了可以实现指数减少错误概率。我们在Amazon Mechanical Turk上对模拟工人以及三个真实图像标注任务的实验表明了我们的教学算法的有效性。
英文摘要：
How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also motivated by challenges in data-driven online education. We propose a natural stochastic model of the learners, modeling them as randomly switching among hypotheses based on observed feedback. We then develop STRICT, an efficient algorithm for selecting examples to teach to workers. Our solution greedily maximizes a submodular surrogate objective function in order to select examples to show to the learners. We prove that our strategy is competitive with the optimal teaching policy. Moreover, for the special case of linear separators, we prove that an exponential reduction in error probability can be achieved. Our experiments on simulated workers as well as three real image annotation tasks on Amazon Mechanical Turk show the effectiveness of our teaching algorithm.
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