Connectionists: CFP: ICML'13 Workshop: Machine Learning Meets Crowdsourcing

[Qiang Liu]

CALL FOR CONTRIBUTIONS

ICML '13 Workshop: Machine Learning Meets Crowdsourcing
Date: June 21, 2013
Location: Atlanta, USA
http://www.ics.uci.edu/~qliu1/MLcrowd_ICML_workshop/

Deadline: 15th Apr, 2013
Acceptance notification: 15th May, 2013.
Workshop date: 21st June, 2013.

Our ability to solve challenging scientific and engineering problems relies
on a mix of human and machine intelligence. The machine learning (ML)
research in the past two decades has created a set of powerful theoretical
and empirical tools for exploiting machine intelligence. On the other side,
the recent rise of human computation and crowdsourcing approaches enables
us to systematically harvest and organize human intelligence, for solving
problems that are easy for human but difficult for computers. The past few
years have witnessed widespread use of the crowdsourcing paradigm,
including task-solving platforms like Amazon Mechanical Turk and
CrowdFlower, crowd-powered scientific projects like GalaxyZoo and Foldit
game, as well as various successful crowdsourcing business such as
crowdfunding and open Innovation, to name a few.

This trend yields both new opportunities and challenges for the machine
learning community. On one side, crowdsourcing systems provide machine
learning researchers with the ability to gather large amount of valuable
data and information, leading advances in challenging problems in areas
like computer vision and natural language processing. On the other side,
crowdsourcing confronts challenges on increasing its reliability,
efficiency and scalability, for which machine learning can provide power
computational tools. More importantly, building systems that seamlessly
integrate machine learning and crowdsourcing techniques can greatly push
the frontier of our ability to solve challenging and large-scale problems.

The goal of this workshop is to bring together experts on fields related to
crowdsourcing such as economics, game theory, cognitive science and
human-computer interaction with the machine learning community to have a
workshop focused on areas where crowdsourcing can contribute to machine
learning and vice versa. We are interested in a wide variety of topics,
including but not limited to:

    ---- State of the field. What are the emerging crowdsourcing tasks and
new opportunities for machine learning? What are the latest and greatest
tasks being tackled by crowdsourcing and human intelligence and how do
these tasks highlight the need for new machine learning approaches that
aren’t being studied already?

     ---- Integrating machine and human intelligence. How to build
practical systems that seamlessly integrate machine and human intelligence?
Machine learning algorithms can help the crowdsourcing component to manage
work flows and control workers’ qualities, while the crowds can be used to
handle the tasks that are difficult for machines to adaptively boost the
performance of machine learning algorithms.

      ---- Machine learning for crowdsourcing. Many machine learning
approaches have been applied to crowdsourcing on problems such as output
aggregation, quality control, work flow management and incentive mechanism
design. We expect to see more machine learning contribution to
crowdsourcing, either by novel ML methods, or on new crowdsourcing problems.

      ---- Crowdsourcing for machine learning. Machine learning largely
relies on big and high quality data, which can be provided by crowdsourcing
systems, perhaps in an automatic and adaptive way. Also, most machine
learning algorithms have many design choices that require human
intelligence, including tuning hyper-parameters, selecting score functions,
and designing kernel functions.
How can we systematically "outsource" these typically expert-level design
choices to the crowds in order to achieve results that match expert-level
human experience?

    ---- Crowdsourcing complicated tasks. How to design work flows and
aggregate answers in crowdsourcing systems that collect structured labels,
such as bounding box annotations in computer vision, protein folding
structures in biology, or solve complicated tasks such as proof reading,
and machine translation? How can machine learning provide help in these
cases?

    ---- Theoretical analysis. There are many open theoretical questions in
crowdsourcing that can be addressed by statistics and learning theory.
Examples include analyzing label aggregation algorithms such as EM, or
budget allocation strategies.


Invited Speakers
~~~~~~~~~~~~~~~~~~

- Jeffrey P. Bigham. University of Rochester
- Yiling Chen. Harvard University
- Panagiotis G. Ipeirotis. NYU Stern School of Business
- Mark Steyvers. UC Irvine
- More ...



Submission Details
~~~~~~~~~~~~~~~~~

Submissions should follow the ICML format (http://icml.cc/2013/wp-**
content/uploads/2012/12/**icml2013stylefiles.tar.gz<http://icml.cc/2013/wp-content/uploads/2012/12/icml2013stylefiles.tar.gz>)
and are encouraged to be up to eight pages. Papers submitted for review do
not need to be anonymized. There will be no official proceedings, but the
accepted papers will be made available on the workshop website. Accepted
papers will be either presented as a talk or poster.We welcome submissions
both on novel research work as well as extended abstracts on work recently
published or under review in another conference or journal (please state
the venue of publication in the later case); we particularly encourage
submission of visionary position papers on the emerging trends on
crowdsourcing and machine learning.

Please submit papers in PDF format at
https://cmt.research.microsoft.com/MLCROWD2013/.

Important Dates
~~~~~~~~~~~~~~

Extended abstract submission deadline: 15th Apr, 2013.
Acceptance notification: 15st May, 2013.
Workshop date: 21st June, 2013.


Workshop Organizers:
~~~~~~~~~~~~~~~~~~~~~

Paul Bennett, Dengyong Zhou, John Platt, Microsoft Research Redmond

Qiang Liu, UC Irvine

Xi Chen, Qihang Lin, CMU


Contact:
~~~~~~~
lqiang67+MLcrowdworkshop at gmail.com








-- 
Qiang Liu
Ph.D student
Information & Computer Department
University of California, Irvine
Homepage: http://www.ics.uci.edu/~qliu1/
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