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<p>Dear Autonians,</p>
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<div style="margin-top:0; margin-bottom:0"><font face="Calibri,Arial,Helvetica,sans-serif"><span style="background-color:transparent; font-variant:normal">This workshop might be of interest to some of you, including those working on applications of machine
learning for development, as well as folks researching algorithms that work under constraints that are common to development settings, e.g. limited computational power. We would also appreciate your help spreading the word! Regards,
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong>Call for Papers - NIPS 2017 Workshop on Machine Learning for the Developing World</strong></span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">*********************************************************************************</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Workshop on Machine Learning for the Developing World, NIPS 2017</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Date: December 8th, 2017</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Location: Long Beach, California, USA</span></font></div>
<div style="margin-top:0; margin-bottom:0"><a href="https://sites.google.com/site/ml4development/" target="_blank"><font color="#1155CC" face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal"><span style="text-decoration:underline">https://sites.google.com/site/ml4development/</span></span></font></a></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">*********************************************************************************</span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong><span style="text-decoration:underline">Call for papers</span></strong></span></font><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong>:
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">This one-day workshop is focussed on machine learning for the developing world (ML4D). We will discuss impactful
applications of machine learning to address core global development concerns, as well as limitations to ML in developing countries and novel algorithms inspired by development challenges, such as limited computational capacity.</span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">We invite researchers to submit their recent work on this topic, including:</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">* Applications of ML to development issues including health, education, institutional integrity, violence mitigation,
economics, societal analysis, and environment.</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">* Novel ML techniques inspired by limitations in developing countries.</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">* Limitations and risks of data science and ML for development.</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">* Practical systems using ML in developing regions.</span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Please submit 2-4 page extended abstracts to ml4d.nips@gmail.com, following the
</span></font><a href="https://nips.cc/Conferences/2017/PaperInformation/StyleFiles" target="_blank"><font color="#1155CC" face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal"><span style="text-decoration:underline">NIPS
style</span></span></font></a><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal"> guidelines. Accepted papers will be presented as posters or contributed talks, and may optionally be published in an arXiv
proceedings.</span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong><span style="text-decoration:underline">Key dates:</span></strong></span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Submission deadline: October 20, 2017</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Acceptance notification: November 1, 2017</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Workshop: December 8, 2017</span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong><span style="text-decoration:underline">Speakers</span></strong></span></font><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong>:</strong></span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">-- Emma Brunskill (Stanford)</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">-- Stefano Ermon (Stanford)</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">-- Daniel Neill (CMU)</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">-- Patrick Ball (Human Rights Data Analysis Group)</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">-- Jen Ziemke (International Network of Crisis Mappers)</span></font></div>
<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">-- John Quinn (UN Global Pulse)</span></font><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal"><br>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong><span style="text-decoration:underline">Workshop overview</span></strong></span></font><font face="Cambria"><span style="background-color:transparent; font-variant:normal"><strong>:</strong></span></font>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Six billion people live in developing world countries. The unique development challenges faced by these regions
have long been studied by researchers ranging from sociology to statistics and ecology to economics. With the emergence of mature machine learning methods in the past decades, researchers from many fields - including core machine learning - are increasingly
turning to machine learning to study and address challenges in the developing world. This workshop is about delving into the intersection of machine learning and development research.</span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Machine learning present tremendous potential to development research and practice. Supervised methods can provide
expert telemedicine decision support in regions with few resources; deep learning techniques can analyze satellite imagery to create novel economic indicators; NLP algorithms can preserve and translate obscure languages, some of which are only spoken. Yet,
there are notable challenges with machine learning in the developing world. Data cleanliness, computational capacity, power availability, and internet accessibility are more limited than in developed countries. Additionally, the specific applications differ
from what many machine learning researchers normally encounter. The confluence of machine learning's immense potential with the practical challenges posed by developing world settings has inspired a growing body of research at the intersection of machine learning
and the developing world.</span></font></div>
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">This one-day workshop is focussed on machine learning for the developing world, with an emphasis on developing
novel methods and technical applications that address core concerns of developing regions. We will consider a wide range of development areas including health, education, institutional integrity, violence mitigation, economics, societal analysis, and environment.
From the machine learning perspective we are open to all methodologies with an emphasis on novel techniques inspired by particular use cases in the developing world.
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<div style="margin-top:0; margin-bottom:0"><font face="Cambria"><span style="background-color:transparent; font-weight:normal; font-variant:normal">Invited speakers will address particular areas of interest, while poster sessions and a guided panel discussion
will encourage interaction between attendees. We wish to review the current approaches to machine learning in the developing world, and inspire new approaches and paradigms that can lay the groundwork for substantial innovation.</span></font></div>
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<div style="font-family:Tahoma; font-size:13px">Mar<span style="line-height:18px; background-color:rgb(254,251,243)">ķa De Arteaga </span>
<div>PhD Student in Machine Learning and Public Policy</div>
<div>Carnegie Mellon University</div>
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