ICML 2020 Announces Outstanding Paper Awards

Organizers of the 37th International Conference on Machine Learning (ICML) have announced their Outstanding Paper awards, recognizing papers from the current conference that are "strong representatives of solid theoretical and empirical work in our field." A total of 1,088 papers out of 4,990 submissions made it to the prestigious machine learning conference. The acceptance rate of 21.8 percent is slightly lower than 2019's 22.6 percent (774 accepted papers from 3,424 submissions), and it seems likely the drastic increase in submissions helped contribute to this. Authors: Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya Institutions: NVIDIA Research, Stanford University, Bar Ilan University Abstract: Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given to the common case where set elements themselves adhere to their own symmetries.