This ICML workshop seeks to broaden the role of geometric models and thinking in machine-learning research.

The interplay between machine learning and geometry is an active field of research drawing the attention of researchers from many fields as it offers not only beautiful mathematical and statistical theory but also substantial impact on important real-world problems in machine learning. Examples of this interplay include, but are not limited to:

• geometric insights into deep learning (helping us to better understand and improve these models);
• statistical models that respect and exploit the constraints of geometric data ("statistics on manifolds");
• (Riemannian) manifold learning;
• viewing probability distributions as points on a nonlinear manifold ("information geometry");
• optimization over nonlinear manifolds;
• Wasserstein spaces and their applications;
• understanding the effect of encoding group invariances (e.g. rotation invariance) on the intrinsic geometry of the data space, which becomes a quotient space.

This ICML workshop is co-located with ICML, IJCAI and AAMAS in Stockholm.

The Bosch center for AI generously sponsors a prize for the best abstract, which will be presented at the workshop.

The meeting is now passed -- thanks to all attendees for fantastic discussions. Also, congratulations to William Guss for winning the Best Contribution Award (sponsored by Bosch Center for Artificial Intelligence)!