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\workshopHeader{The Silver Lining: Learning from Unexpected Results}
{Joaquin Vanschoren and Wouter Duivesteijn} % organisers
{09:30--16:00} % please use this time format everywhere
{Room: Wills, 3.33}
{Monday}

% Add an introduction to the workshop here
\workshopIntro{
This workshop is dedicated to the proposition that insight often begins with
unexpected results.  Successful methods do not simply fall from the sky:
they are discovered based on clues gathered by trying several ideas,
learning from surprising results, and building an understanding of what
works, what does not, and why. Unexpected results chart the boundaries of our knowledge: they identify
errors, reveal false assumptions, and force us to dig deeper.

This process is rarely mentioned in the machine learning and data mining
discourse, meaning that such insight is essentially lost. Ironically, 
while we have long understood that learning from only
positive results is substantially harder than learning from both positives
and negatives, there exists a publication bias that favours (incremental)
successes over novel discoveries of why some ideas, while intuitive and
plausible, do not work.

Good science consists of carefully designed experiments, systematic procedures, and honest evaluations, even if not all results are positive. As a scientific area where empirical methods dominate, it is a given that people try many ideas and obtain surprising results in the experimental stage. We can learn a lot if we analyze scientifically why an intuitive and plausible experiment did not work as expected.

Just as every cloud has a silver lining, these unexpected results define the
actual boundaries of our field: they highlight what we do not yet
understand, and often point to interesting and open problems that ought to
be explored. With this workshop, we want to give a voice to those
unexpected results that deserve wider dissemination.
}
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\timeTitlePeople{09:30--10:30}
{Opening Keynote: Unexpected Results in Monte-Carlo Tree Search}
{Olivier Teytaud (Universit\'{e} Paris-Sud)}

\timeTitle{10:30--11:00}{Coffee Break}

\timeTitlePeople{11:00--11:30}
{Generation of an Empirical Theory for Positive-Versus-Negative Ensemble Classification}
{Patricia Lutu (University of Pretoria)}

\timeTitlePeople{11:30--12:00}
{How to Make the Most of Result Evaluation?}
{Ana Costa e Silva (University of Porto)}

\timeTitle{12:00--13:30}{  Lunch (not provided)}

\timeTitlePeople{13:30--14:30}
{Keynote: On the Search for and Appreciation of Unexpected Results in Data Mining
Research (or: Science -- we Might be Doing it Wrong)}
{Albrecht Zimmermann (University of Leuven)}

\timeTitlePeople{14:30--15:00}
{Adventures in Feature Selection on an Industrial Dataset\ldots and Ensuing General Discoveries}
{George Forman (Hewlett-Packard Labs)}

\timeTitle{15:00--16:00}{Panel Discussion}

%\timeTitle{16:00--16:30}{Coffee Break}

%\timeTitle{\startTimeOnly{16:30}}{ECML PKDD Opening Session}

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%\timeTitleText{\startTimeOnly{18:00}}{Workshop dinner at the Beagle pub}{To join us
%  please register your interest with one of the organisers by the end
%  of the lunch break.}

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