Connectionists: [CFP] AAAI-14 Workshop on Sequential Decision-Making with Big Data

[Amir-massoud Farahmand Andre Barreto]

The AAAI-14 Workshop on Sequential Decision-Making with Big Data
held at the AAAI Conference on Artificial Intelligence (AAAI-14), Quebec
City, Canada (July 27-28, 2014)

(Workshop URL: https://sites.google.com/site/decisionmakingbigdata )


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.


Topics
Some potential topics of interest are:
- Reinforcement learning algorithms that deal with one of the
aforementioned categories;
- Bandit problems with high-dimensional action space
- Challenging real-world applications of sequential decision-making
problems that can benefit from big data. Example domains include robotics,
adaptive treatment strategies for personalized health care, finance,
recommendation systems, and advertising.


Format
The workshop will be a one-day meeting consisting of invited talks, oral
and poster presentations from participants, and a final panel-driven
discussion.


Attendance
We expect about 30-50 participants from invited speakers, contributed
authors, and interested researchers.


Submission
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 decision.making.big.data at gmail.com.
Accepted papers will be presented as posters or contributed oral
presentations.

Important Dates
Paper Submission: April 10, 2014
Notification of Acceptance: May 1, 2014
Camera-Ready Papers: May 15, 2014
Date of Workshop: July 27 or 28, 2014



Organizing Committee
- Amir-massoud Farahmand (McGill University)
- André M.S. Barreto (Brazilian National Laboratory for Scientific
Computing (LNCC))
- Mohammad Ghavamzadeh (Adobe Research and INRIA Lille - Team SequeL)
- Joelle Pineau (McGill University)
- Doina Precup (McGill University)
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