The AAAI-14 Workshop on Sequential Decision-Making with Big Data

July 28, 2014, Québec City, Québec, Canada

(Check the schedule here)

In the 21st century, we live in a world where data is abundant. We would like to use this data to make better decisions in many areas of life, such as industry, health care, business, and government. This opportunity has encouraged many machine learning and data mining researchers to develop tools to benefit from big data. However, the methods developed so far have focused almost exclusively on the task of prediction. As a result, the question of how big data can leverage decision-making has remained largely untouched.

This workshop is about decision-making in the era of big data. The main topic will be the complex decision-making problems, in particular the sequential ones, that arise in this context. Examples of these problems are high-dimensional large-scale reinforcement learning and their simplified version such as various types of bandit problems. These problems can be classified into three potentially overlapping categories:

1. Very large number of data-points. Examples: data coming from user clicks on the web and financial data. In this scenario, the most important issue is computational cost. Any algorithm that is super-linear will not be practical.
2. Very high-dimensional input space. Examples are found in robotic and computer vision problems. The only possible way to solve these problems is to benefit from their regularities. 
3. Partially observable systems. Here the immediate observed variables do not have enough information for accurate decision-making, but one might extract sufficient information by considering the history of observations. If the time series is projected onto a high-dimensional representation, one ends up with problems similar to 2.