Traditionally, information geometry has been concerned with the identification of natural geometric structures of statistical models. It has been demonstrated that their use has a great impact on the quality of statistical methods and learning algorithms. One instance of this is given by the natural gradient method, which improves the learning simply by utilising the natural geometry induced by the Fisher-Rao metric. The general geometric perspective of information geometry had already a great influence on machine learning and is expected to further influence the general field of data science.

This conference will bring together scientists from various fields in order to explore the potential of information geometry for the foundations of data science. In addition to invited keynote presentations of leading experts, it will accommodate contributed oral and poster presentations. The submissions of corresponding extended abstracts (two pages at least and five pages at most) will be reviewed by the scientific committee with regard to their quality and their potential relevance for data science.

The general worldwide situation creates serious challenges for international travel, making long-term planning very difficult. This is why we decided, unfortunately, to run the conference fully virtually. On the other hand, this offers some flexibility in our planning. First of all, we can now extend the deadline for submissions of contributions. The new deadline is July 24th, 2022, at 23:59 (CEST). Furthermore, the conference fee can be further reduced to 60 Euros. All registrations already made will be issued according to this new amount. The authors will receive a notification of the review outcome until August 12th, 2022.

The lectures of the conference will be recorded and made accessible to registered participants, assuming the consent of the speakers.

Confirmed invited speakers

• Kenji Fukumizu (The Institute of Statistical Mathematics, Tokyo, Japan)
• Hideitsu Hino (The Institute of Statistical Mathematics, Tokyo, Japan)
• Dominik Janzing (Amazon Research, Tübingen, Germany)
• Emtiyaz Khan (RIKEN Center for Advanced Intelligence Project, Japan)
• Wuchen Li (University of South Carolina, USA)
• James Martens (DeepMind, London, UK)
• Guido Montúfar (MPI for Mathematics in the Sciences, Germany & UCLA, USA)
• Klaus-Robert Müller (TU Berlin, Germany)
• Masafumi Oizumi (The University of Tokyo, Japan)
• Gabriel Peyré (CNRS & École normale supérieure, France)
• Minh Ha Quang (RIKEN Center for Advanced Intelligence Project, Japan)
• Sho Sonoda (RIKEN Center for Advanced Intelligence Project, Japan)
• Leonard Wong (University of Toronto, Canada)