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:
Some potential topics of interest are:
The workshop will be a one-day meeting consisting of invited talks, oral and poster presentations from participants, and a final panel-driven discussion.
We expect about 30-50 participants from invited speakers, contributed authors, and interested researchers.
We invite researchers from different fields of machine learning (e.g., reinforcement learning, online learning, active learning), optimization, systems (distributed and parallel computing), as well as application-domain experts (from e.g., robotics, recommendation systems, personalized medicine, etc.) to submit an extended abstract (maximum 4 pages in AAAI format) of their recent work to firstname.lastname@example.org. Accepted papers will be presented as posters or contributed oral presentations.