Active Information Acquisition

Abstract

We propose a general framework for sequential and dynamic acquisition of useful information in order to solve a particular task. While our goal could in principle be tackled by general reinforcement learning, our particular setting is constrained enough to allow more efficient algorithms. In this paper, we work under the Learning to Search framework and show how to formulate the goal of finding a dynamic information acquisition policy in that framework. We apply our formulation on two tasks, sentiment analysis and image recognition, and show that the learned policies exhibit good statistical performance. As an emergent byproduct, the learned policies show a tendency to focus on the most prominent parts of each instance and give harder instances more attention without explicitly being trained to do so.

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Recurrent Models of Visual Attention
• Learning to Search Better Than Your Teacher
• Learning Wake-Sleep Recurrent Attention Models

Cited by in corpus (5)

• Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
• Active Feature Acquisition with Generative Surrogate Models
• Dynamic Measurement Scheduling for Event Forecasting using Deep RL
• Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL
• Towards Robust Active Feature Acquisition