Efficient Deep Learning with Humans in the Loop
While deep learning produces supervised models with unprecedented predictive performance on many tasks, under typical training procedures, advantages over classical methods emerge only with large datasets. The extreme data-dependence of reinforcement learners may be even more problematic. Millions of experiences sampled from video-games come cheaply, but human-interacting systems can’t afford to waste so much labor. In this talk I will discuss several efforts to increase the labor-efficiency of learning from human interactions. Specifically, I will cover work on learning dialogue policies, deep active learning for natural language processing, learning from noisy singly-labeled data, and active learning with partial feedback.