Connectionists: ESANN 2021 SS 2nd CFP - Complex Data: Learning Trustworthily, Automatically, and with Guarantees

[Luca Oneto]

[Apologies if you receive multiple copies of this CFP]

Call for papers: special session on "Complex Data: Learning Trustworthily,
Automatically, and with Guarantees" at ESANN 2021 - https://www.esann
.org/special-sessions

European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN 2021).
6-8 October 2021, Bruges, Belgium - http://www.esann.org

DESCRIPTION:
Machine Learning (ML) achievements enabled automatic extraction of
actionable information from data in a wide range of decision-making
scenarios (e.g. health care, cybersecurity, and education). ML models are
nowadays ubiquitous pushing even further the process of digitalization and
datafication of the real and digital world producing more and more complex and
interrelated data. This demands for improving both ML technical aspects
(e.g. design and automation) and human-related metrics (e.g. fairness,
robustness, privacy, and explainability), with performance guarantees at
both levels.
The aforementioned scenario posed three main challenges: (i) Learning from
Complex Data (i.e. sequence, tree and graph data), (ii) Learning
Trustworthily, and (iii) Learning Automatically with Guarantees. The scope
of this special session is then to address one or more of these challenges
with the final goal of Learning Trustworthily, Automatically, and with
Guarantees from Complex Data.
The focus of this special session is to attract both solid contributions or
preliminary results which show the potentiality and the limitations of new
ideas, refinements, or contaminations between the different fields of
machine learning and other fields of research in solving real world
problems. Both theoretical and practical results are welcome to our special
session.

TOPICS OF INTEREST:
- efficient and effective models capable of directly learning from
data natively
structured or
collected from interrelated heterogeneous sources (e.g. social and
relational data, knowledge graphs), characterized by entities, attributes,
and relationships, without relying on human skills to encode this
complexity into a rich and expressive (vectorial) representation;
- design ML models from a human-centered perspective, making ML trustworthy
by design, by removing human biases from the data (e.g. gender
discrimination), increasing robustness (e.g. to adversarial data perturbation),
preserving individuals’ privacy (e.g. protecting ML models from
differential attacks), and increasing transparency (e.g. via ML models and
output explanation);
- automatizing the ML design and deployment parts which are currently
handcrafted by highly skilled and trained specialists. For this reason, ML
is required to be empowered with self-tuning properties (e.g. architecture
and hyperparameter automatic selection), understanding and guaranteeing the
final performance (e.g. with worst case and statistical bounds) with
respect to both technical and human relevant metrics.

SUBMISSION:
Prospective authors must submit their paper through the ESANN portal
following the instructions provided in https://www.esann.org/node/6  Each
paper will undergo a peer reviewing process for its acceptance.

IMPORTANT DATES:
Submission of papers: 10 May 2021
Notification of acceptance: 20 July 2019
ESANN conference: 6-8 April 2019

SPECIAL SESSION ORGANISERS:
Luca Oneto, University of Genoa (Italy)
Nicolò Navarin, University of Padua (Italy)
Battista Biggio, University of Cagliari (Italy)
Federico Errica, University of Pisa (Italy)
Alessio Micheli, University of Pisa (Italy)
Franco Scarselli, University of Siena (Italy)
Monica Bianchini, University of Siena (Italy)
Alessandro Sperduti, University of Padua (Italy)

----------------------------------------
Prof. Luca Oneto
DIBRIS - University of Genoa
web: www.lucaoneto.it
e-mail: luca.oneto at unige.it
----------------------------------------
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20210318/a077267a/attachment.html>