Connectionists: [CFP] AAAI Workshop on Sequential Decision-Making with Big Data (Deadline: April 10)

[Amir-massoud Farahmand Andre Barreto]

This is just a friendly reminder that the paper submission deadline for
this workshop is on April 10, 2014.
We also have an exciting list of confirmed invited speakers. Make sure that
you participate!
-am



*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



*Invited Speakers*- Emma Brunskill <http://www.cs.cmu.edu/~ebrun/> (Carnegie
Mellon University)
- Lihong Li <http://research.microsoft.com/en-us/people/lihongli/> (Microsoft
Research)
- Richard Sutton <http://webdocs.cs.ualberta.ca/~sutton/> (University of
Alberta)
- And a few others to be confirmed later.



*Organizing Committee*- Amir-massoud
Farahmand<http://academic.sologen.net/Home.html> (Carnegie
Mellon University)
- André M.S. Barreto <http://www.cs.mcgill.ca/~amsb/> (Brazilian National
Laboratory for Scientific Computing (LNCC))
- Mohammad Ghavamzadeh<http://chercheurs.lille.inria.fr/~ghavamza/my_website/About_Me.html>
(Adobe
Research and INRIA Lille - Team SequeL)
- Joelle Pineau <http://www.cs.mcgill.ca/~jpineau/> (McGill University)
- Doina Precup <http://www.cs.mcgill.ca/~dprecup/> (McGill University)
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