ENBIS-24 Leuven Conference

Session

Machine learning I

16 Sept 2024, 13:30

Leuven, Belgium

Conveners

Machine learning I

• Marco P. Seabra dos Reis (Department of Chemical Engineering, University of Coimbra)

19. Practical views on quantifying performance of prediction models

Mr Jan-Willem Bikker (CQM)

16/09/2024, 13:30

Machine Learning

In recent decades, machine learning and industrial statistics have moved closer to each other. CQM, a consultancy company, performs projects in supply chains, logistics, and industrial R&D that often involve building prediction models using techniques from machine learning. For these models, challenges persist, e.g. if the dataset is small, has a group structure, or is a time series. At the...

59. Random forest and transfer learning based methods for causality.

Véronique Maume-Deschamps (Institut Camille Jordan, Université Claude Bernard Lyon 1)

16/09/2024, 13:50

Machine Learning

Conditional Average Treatment Effect (CATE) is widely studied in medical contexts. It is one tool used to analyze causality. In the banking sector, the interest for causality methods increases. As an example, one may be interested in estimating the average effect of a financial crisis on credit risk, conditionally to macroeconomic as well as internal indicators. On one other hand, transfer...

121. Applications of machine learning methods for parameter estimation in spreading processes on hypergraphs

András Zempléni (Eötvös Loránd University, Budapest)

16/09/2024, 14:10

Machine Learning

In spreading processes such as opinion spread in a social network, interactions within groups often play a key role. For example, we can assume that three members of the same family have higher chance to persuade a fourth member to change their opinion than three friends of the same person who do not know each other, and hence who do not belong to the same community. The other way around, in a...