Smartphone Interruptibility Using Density-Weighted Uncertainty Sampling with Reinforcement Learning

We present the In-Context application for smart-phones, which combines signal processing, active learning, and reinforcement learning to autonomously create a personalized model of interruptibility for incoming phone calls. We empirically evaluate the system, and show that we can obtain an average of 96.12% classification accuracy when predicting interruptibility after a week of training. In contrast to previous work, we leverage density-weighted uncertainty sampling combined with a reinforcement learning framework applied to passively collected data to achieve comparable or superior classification accuracy using many fewer queries issued to the user.