BFTA 2023

The School

The BELIEF school is a biennial event organized by the Belief Functions and Applications Society (BFAS) that offers a unique opportunity for students and researchers to learn about fundamental and advanced aspects of the theory of belief functions (also referred to as Dempster-Shafer theory, or evidence theory), a formalism for reasoning with uncertainty.

The school will be organized around a set of lectures by prominent researchers. Lectures will gradually tackle basic to more advanced theoretical concepts. They will also highlight the links with other uncertainty theories such as random sets and possibility theory, and present applications of belief functions in various domains including machine learning, information fusion, statistical inference and materials science.

Important dates

• May 1, 2023: Registration opens
• July 31, 2023: Deadline for early-bird registration
• September 15, 2023: Deadline for registration

Fees

• Students 190€ (early-bird) / 240€
• Regular 290€ (early-bird) / 340€

Financial support

Financial support will be offered by BFAS to some students for attending the school. Details of the application procedure will be announced soon.

The Lectures

Introduction to the theory of belief functions

Thierry Denœux, Université de technologie de Compiègne, France.

Evidential clustering

Thierry Denœux, Université de technologie de Compiègne, France.

Differential privacy for belief functions

Chunlai Zhou, Renmin University of China.

Pattern classification with belief functions

Zhunga Liu, Northwetsern Polytechnical Universityn Xi’an, China.

Information fusion in the theory of evidence

Frédéric Pichon, Université d'Artois, France.

Application of possibility theory to optimization and decision making

Masahiro Inuiguchi, Osaka University, Japan.

Epistemic random fuzzy sets: theory and application to machine learning

Thierry Denœux, Université de technologie de Compiègne, France.

Applications of Belief Functions for Exploring Novel Materials

Hieu Chi Dam, JAIST, Japan.

Old and new developments in (consonant) belief functions for statistical inference

Ryan Martin, North Carolina State University, USA.

Graphical Belief Function Models: Theory, Computation and Applications

Prakash Shenoy, School of Business, University of Kansas, USA.

Measuring inconsistency in evidence theory

Anne-Laure Jousselme, CS Group research lab, France.

Application of belief functions to ensemble classification and recommendation

Van-Nam Huynh, JAIST, Japan.