The International Conference on Machine Learning (ICML) received nearly 5,000 submissions for its 2020 conference and accepted 1,088 papers. Machine Learning Center at Georgia Tech (ML@GT) researchers authored nine accepted papers.
The papers explore topics like privacy, semantics in predictive agents, data science, and artificial intelligence. One paper, Boosting Frank-Wolfe by Chasing Gradients, proposes a new state-of-the-art algorithm for constrained optimization, an area already addressed in work accepted in 2019.
“I think we’re going to see a lot more work moving in the direction of general artificial intelligence, especially work that is trying to combine learning and reasoning,” said Le Song, an associate director at ML@GT.
ML@GT faculty members are also participating in the conference outside of presenting accepted papers. School of Interactive Computing and ML@GT assistant professor Judy Hoffman served as a conference area chair. Rachel Cummings, an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and ML@GT, co-organized the workshop ECOnomics of Privacy and Data Labor (EcoPaDL.)
Song is also organizing a workshop, Bridge Deep Learning and Reasoning. Turing Award winner Yoshua Bengio will give a keynote at the workshop to explain this emerging trend.
The conference was originally planned to be held in Vienna, Austria but was moved online due to Covid-19. It will virtually take place July 12-18th.
For more information on Georgia Tech’s accepted work, visit our ML@GT ICML landing page and the ML@GT at ICML YouTube playlist.
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