I am a graduate student in the EECS department at MIT and a member of CSAIL and the Machine Learning group.
I am fortunate to be advised by Stefanie Jegelka. Previously, I was an undergraduate at UBC, where I was very fortunate to be advised by Nick Harvey. I also seasonally visit Ken-ichi Kawarabayashi in Tokyo. They are the most interesting and attractive researchers in theory of intelligence: they inspire me to seek the root of the world.
Email me if you have any questions about my papers or code, or if you would like to collaborate with me.
- How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka.
International Conference on Learning Representations (ICLR) 2019. Oral Presentation.
[Paper] [arXiv] [code]
- Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka.
International Conference on Machine Learning (ICML) 2018. Long talk.
[Paper] [arXiv] [Code (soon)]
- Distributional Adversarial Networks
Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra.
International Conference on Learning Representations workshop track (ICLR) 2018.
- Generating Random Spanning Trees via Fast Matrix Multiplication
Nicholas J. A. Harvey and Keyulu Xu.
Latin American Theoretical Informatics Symposium (LATIN) 2016.
I have lived and worked in some of the most exciting cities in the world -- Vancouver, Tokyo, NYC and Shanghai.
Some recent talks by Keyulu, with video if available.
- Powerful Graph Neural Networks, at Kyoto University, Graduate School of Informatics, Kashima & Yamada Lab. [Slides]
- Representation Learning on Graphs with Jumping Knowledge Networks, at RIKEN AIP, Nihonbashi, Tokyo. [Slides]
- Representation Learning on Graphs with Jumping Knowledge Networks, at ICML 2018, Stockholm, Sweden. [Slides] [Video]
- Generating Random Spanning Trees via Fast Matrix Multiplication, at LATIN 2016, Ensenada, Mexico. [Slides]