Call for papers

Special Session on Machine learning and decision making under weakly structured information

at the 11th International Conference on Soft Methods in Probability and Statistics (SMPS 2024)

Salzburg (Austria), September 4-6, 2024

Session topic and goal This session deals with machine learning problems that appear under weak information structures and, therefore, fail to be handled by traditional methods. The reason for the weak structure can be manifold, ranging from complex data and non-typical scales of measurement to generalized uncertainty models such as imprecise probabilities. In particular, the topics of the session include (but are not limited to):

•  Imprecise probabilities in ML and statistics

•  Preferences in ML and statistics

•  Decision theoretic approaches in ML and statistics

•  Formal concept analysis in ML and statistics

•  Non-standard data (e.g. ordinal data, mixed-scaled data)

•  (Generalized) Bayesian methods in ML and statistics

•  Uncertainty quantification in ML and statistics

•  Robustness in decision theory

•  Robust statistics

Dates Paper submission deadline: February 29, 2024 Author notification: April 21, 2024 Conference: September 4-6, 2024

Submissions When submitting the paper, choose the session Machine learning and decision making under weakly structured information in the conference management tool. Papers should be 6-8 pages. All required information can be found on the conference homepage www.smps2024.com<https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.smps2024.com%2F&data=05%7C02%7Cuai%40engr.orst.edu%7Cb6c6b2dc04804359fa4808dc24302ad0%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638425036495585699%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=5TF78LKTY2DuGQL0EAg6kZ1auARi8BBYtkC4VMo8xAM%3D&reserved=0>.

Organizers Please let us know if you plan to submit a contribution to this session as soon as possible. Questions or comments can be addressed to:

Thomas Augustin, Christoph Jansen, Georg Schollmeyer Ludwig-Maximilians-Universität München (Germany) {thomas.augustin,christoph.jansen,georg.schollmeyer}@stat.uni-muenchen.de