Thanks to Thanh Binh Nguyen for Orléans pictures.
One-day Workshop on
Machine Learning and Explainability
8 October 2018
Hotel Dupanloup, Orléans, France
With the advent of new deep learning
methods, models learned
by Machine Learning are often seen as blakboxes that
are difficult to interpret. The ability to understand the meaning and
properties of results produced by ML-based tools, in other words the
explainability becomes crucial both for ethical purposes and to allow
their use in operational conditions. The main objective of this
workshop is to bring together researchers working on the topic of
explicability for Machine Learning. It will present some contributions
to the field and allow to discuss new challenges in this domain.
Participants have the opportunity to make an oral presentation. Oral
presentations will be accepted mainly based on the relevance to the
topic of the workshop.The edition of post-proceedings will be
considered.
If you wish
to make an oral presentation, please send a 1-page summary of your talk
to Christel Vrain
before September 15.
Scientific Committee
Christel
Vrain, University of Orléans
Christophe Denis,
EDF R&D and Sorbonne University
Jean-Gabriel Ganascia,
Sorbonne Université, LIP6
Florence
d'Alché-Buc,
Télécom ParisTech
Organization
Christel Vrain,
University of Orléans
Administration
Isabelle Renard,
University of
Orléans
Invited speakers
- Pr Stephen Muggleton, Imperial College London
- Pr Jean-Daniel Zucker, Sorbonne Université, IRD, UMMISCO
Registration is free but mandatory (before October, 2) for
organization reasons. Lunch is not included is the registration
but there are many
restaurants not far from Hotel Dupanloup, mainly in Rue de Bourgogne
This workshop is supported by
- Artificial Intelligence Journal
- working group on Explainability of GDR IA
- LIFO, University of Orléans
- ICVL Fédération Informatique Centre Val de Loire