<div dir="ltr"><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">FINAL CALL FOR CONTRIBUTIONS</span></font></p>

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<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><span>The <span>AutoML</span></span> Workshop @ ICML 2014</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Beijing, China, June 25/26, 2014</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Web:</span><a href="http://www.bayesianoptimization.org" style="text-decoration:none" target="_blank"><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"> </span></a><a href="http://icml2014.automl.org" style="text-decoration:none" target="_blank"><span style="font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline">http://icml2014.<span><span>automl</span></span>.org</span></a><a href="http://www.bayesianoptimization.org" style="text-decoration:none" target="_blank"><span style="font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline"></span></a></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Email: <a href="mailto:icml2014@automl.org" target="_blank">icml2014@<span><span>automl</span></span>.org</a></span></font></p>

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<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">----------------------------------------------------------------</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Important Dates:</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Submission deadline: Friday 25 April, 2014 </span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Notification of acceptance: Friday 16 May, 2014</span></font></p>

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<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">----------------------------------------------------------------</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Workshop Overview:</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Machine

 learning has achieved considerable success, but this success crucially 

relies on human machine learning experts to select appropriate features,

 workflows, <span>ML</span> paradigms, algorithms, and 

algorithm hyperparameters. Because the complexity of these tasks is 

often beyond non-experts, the rapid growth of machine learning 

applications has created a demand for machine learning methods that can 

be used easily and without expert knowledge. We call the resulting 

research area that targets progressive automation of machine learning </span><span style="vertical-align:baseline;font-variant:normal;font-style:italic;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><span><span>AutoML</span></span></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">.</span></font></p>

<div>

<font><br><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span></font><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">

<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><span><span>AutoML</span></span> aims to automate many different stages of the machine learning process. Relevant topics include:</span></font></p>

</div><font><div><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Model selection, hyper-parameter optimization, and model search</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  <br>

  - Representation learning and automatic feature extraction / construction </span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Reusable workflows and automatic generation of workflows</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Meta learning and transfer learning</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Automatic problem "ingestion" (from raw data and miscellaneous formats)</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Feature coding/transformation to match requirements of different learning algorithms</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Automatically detecting and handling skewed data and/or missing values</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Automatic leakage detection</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Matching problems to methods/algorithms (beyond regression and classification)</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Automatic acquisition of new data (active learning, experimental design)</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span></div>

<span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">

  - Automatic report writing (providing insight from the automatic data analysis)</span></font><br><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><div>

<span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">

  - User interfaces for <span><span>AutoML</span></span> (e.g., “</span><span style="vertical-align:baseline;font-variant:normal;font-style:italic;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Turbo Tax</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"> for Machine Learning”)</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Automatic inference and differentiation</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Automatic selection of evaluation metrics </span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Automatic creation of appropriately sized and stratified train, validation, and test sets</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Parameterless, robust algorithms </span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span></div>

<span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">

  - Automatic algorithm selection to satisfy time/space constraints at train- or run-time</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br>

  - Run-time wrappers to detect data shift and other causes of prediction failure</span><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span><br>

<span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span></font><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">

<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><br></span></font></p><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt">

<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">We

 encourage contributions in any of these areas. We welcome 2-page 

short-form submissions and 6-page long-form submissions </span></font><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"> (in either case plus references)</span></span></font>. Submissions 

should be formatted using JMLR Workshop and Proceedings format (an 

example LaTeX file is available on the workshop website <a href="http://icml2014.automl.org" style="text-decoration:none" target="_blank"><span style="font-family:Arial;color:rgb(17,85,204);background-color:transparent;font-weight:normal;font-style:normal;font-variant:normal;text-decoration:underline;vertical-align:baseline">icml2014.automl.org</a><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">).

 We also encourage submissions of previously-published material that is 

closely related to the workshop topic (for presentation only).</span></font></p>

<font><br><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span></font><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify">

<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Confirmed invited speakers:</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Dan Roth: Language designed for novice <span>ML</span> developers </span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Holger Hoos: Programming by Optimization </span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Yoshua Bengio: Representation learning</span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Jasper Snoek: Hyper-parameter optimization </span></font></p>

<p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify"><font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">  - Vikash Masingka: Probabilistic programming<br>

<br>Advisory Committee: James Bergstra, Nando de Freitas, Roman Garnett, Matt Hoffman, Michael Osborne, Alice Zheng</span></font></p><font><br><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal"></span></font><p dir="ltr" style="line-height:1.15;margin-top:0pt;margin-bottom:0pt;text-align:justify">

<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">Organizers: Frank Hutter, Rich Caruana, Rémi Bardenet, Misha Bilenko, Isabelle Guyon,</span></font><i> </i>Balázs Kégl<font><span style="vertical-align:baseline;font-variant:normal;font-style:normal;background-color:transparent;text-decoration:none;font-family:Arial;font-weight:normal">, and Hugo Larochelle</span></font></p>

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