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CALL FOR PAPERS
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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
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Description
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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.
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Topics
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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.
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Submission
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We invite researchers from different fields of machine learning (e.g.,
reinforcement learning, online learning, active learning), optimization,
and systems, as well as application-domain experts, to submit an extended
abstract of their recent work to [email protected]. Papers
must be in AAAI format and be no longer than 4 pages. Accepted papers will
be presented as posters or contributed oral presentations.
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Format
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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.
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Important Dates
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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
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Organizing Committee
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- 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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