Call for Contributions
Weakly Supervised and Cautious Learning (WSCL) workshop
Bridging machine learning and uncertainty management
Workshop Information
Co-located with the 27th European Conference on Artificial Intelligence (ECAI 2024)
Location: Santiago de Compostela, Spain
Dates: 19-20th October 2024
Website: https://sites.google.com/unimib.it/wscl2024/
In recent years, machine learning (ML) has been increasingly applied in a variety of settings, with promise to analyze huge and heterogeneous volumes of data, improve decisional accuracy, and ease human labor. These systems have been predominantly built based on the supervised learning paradigm, which relies on the availability of huge amounts of data, assumed to be reliably labeled by human experts.
However, with this increasing interest has also come the realization that the real-world is often far from the idealized perfection assumed in the supervised paradigm: data can be missing or noisy; supervision can be costly to obtain or have veracity issues; human users could fail at appropriating technologies based on ML, due to the incapability of ML models at reliably conveying their uncertainty.
As a result, recently, increasing interest has been devoted to the development of techniques capable of dealing with these issues. These include uncertainty quantification and cautious learning, where the ML models convey to the users their uncertainty to improve reliability and reduce cognitive biases; as well as weakly supervised learning and its variants, such as incomplete supervision, where only a subset of training data is labeled; imprecise supervision, where the training data is coarse-grained labeled; and inaccurate supervision, where the labels are not always true. At the same time, due to the inherently multidisciplinary nature of these issues, there has been an increasingly deepened dialogue between ML and
neighboring scientific fields, such as knowledge and uncertainty representation, as well as human-computer interaction, crowd-sourcing and active learning.
The aim of this workshop is to explore how machine learning and related methods can handle weak supervision and provide more cautious and reliable support in the presence of data imperfection, as well as encourage broad discussion between ML researchers and experts in neighboring related fields whose underlying principles and foundations are central to allow the functioning of systems built on ML in less than perfect real-world settings.
Topics of Interest
We invite all researchers in AI and neighboring disciplines interested in the interplay between Machine Learning and Uncertainty to submit their work to WSCL workshop, which will be held during 19-20 October 2024, as a satellite event to the 27th European Conference on Artificial Intelligence (ECAI 2024).
We welcome submissions on topics related to both weakly supervised learning and uncertainty quantification. More generally, the topics of interest include:
● Weakly supervised learning (including, learning from noisy data, multi-instance learning, learning from imprecise data, …);
● Uncertainty quantification (including, probabilistic and Bayesian machine learning, conformal prediction, three-way decision, set-valued approaches, possibilistic and evidential machine learning, …);
● Uncertainty management and non-standard theories of uncertainty (including, foundations of probability theory, rough sets, fuzzy sets and possibility theory, evidence theory and belief functions, imprecise probabilities and credal sets, …);
● Conformal prediction and related methods (including, Venn prediction, online learning, compression models, algorithmic information theory);
● Human-AI interaction and uncertainty communication;
● Active learning;
● Crowd-sourcing and data perspectivism
Submission Instructions
We welcome different types of submissions:
● full original papers (up to 7 pages);
● extended abstracts (up to 4 pages);
● already published work;
● review transfer for papers rejected at ECAI main track.
Each accepted submission, irrespective of the type, will be assigned an oral presentation slot.
Articles rejected at the main track of the ECAI 2024 conference, but relevant to the topics of interest of the workshop, may be transferred at the WSCL workshop based on the ECAI reviews. If you want to take advantage of this option, aside from submitting your contribution to the workshop, you should also fill-in the form at https://forms.gle/QPav5rHVMCuSMhFZ9 before the 11th July 2024
Contributions should be submitted through the chairingtool platform, available at the following link:
https://chairingtool.com/conferences/WSCL24/MainTrack/
Original submissions (both full papers and extended abstracts) should be formatted according to the ECAI formatting instructions, using the ECAI 2024 template and formatting requirements specified by ECAI.
The purpose of workshop presentations is to get early feedback on new ideas and to stimulate discussions between researchers interested in weakly supervised and cautious learning. As such, there will be no formal proceedings, but we may post accepted papers on the workshop website for the benefit of the workshop participants if the authors agree to this (and the paper has not already been published). Also, depending on the number of accepted contributions we may decide on organizing a special issue on a relevant scientific journal.
Key Dates
● Submission deadline: 12th July 2024
● Review Transfer submission deadline: 12th July 2024
● Accept/Reject communications: 18th July 2024
● Early registration deadline: 15th August 2024
● Workshop date: 19-20th October 2024
Organizers
● Andrea Campagner, IRCCS Ospedale Galeazzi Sant’Ambrogio (Milan, Italy), andrea.campagner@unimib.it (primary contact person)
● Davide Ciucci, University of Milano-Bicocca (Milan, Italy), davide.ciucci@unimib.it
● Sébastien Destercke, Heudyasic Laboratory, CNRS (Compiègne, France), sebastien.destercke@hds.utc.fr
●Luciano Sànchez, University of Oviedo(Oviedo,Spain), luciano@uniovi.es
July 5th, 2024 
Daniela Lopez de Luise 
CARTILLA-INFORMATIVA-ESPECIALIZACXION-EN-GESTION-DE-SW_-2024-2°cohorte.pdf
