Programme of Wednesday, 21 June

Index

Invited Session
Statistical Machine Learning for environmental applications
Organizer/Chair: Michela Cameletti (Università di Bergamo)
Discussant: Francesco Finazzi (Università di Bergamo)
Room: T30
Floor: ground
Short summary: This session is about the use of machine learning and deep learning methods as an alternative to (or integrated with) standard approaches for environmental data, such as for example kriging and spatial point pattern models. These new approaches are appreciated thanks to their flexibility and can be useful for modeling complex spatial or spatio-temporal data. However, some concerns remain with respect to interpretability and uncertainty quantification.

#	Papers
1.	Gaussian Processes and Deep Neural Networks for Spatial Prediction Author(s)  Alex Cucco   Luigi Ippoliti   Nicola Pronello   Pasquale Valentini   Carlo Zaccardi
2.	How can we explain Random Forests in a spatial framework? Author(s)  Xavier Barber   Natalia Golini   Luca Patelli
3.	Recent approaches in coupling deep learning methods with the statistical analysis of spatial point patterns Author(s)  Abdollah Jalilian   Jorge Mateu