About Me
I am a fifth year Ph.D. candidate at School of Information, University of Michigan, Ann Arbor. My advisor is Prof. Qiaozhu Mei. Prior to UMich, I got my Bachelor’s degree at Tsinghua University, advised by Prof. Jie Tang.
I’m fascinated in discovering succinct descriptions of objects and their relationships, from which machine learning can benefit with improved computational or statistical efficiency. I’m particularly interested in machine learning models for graph-structured data, a general representation of data. I also work on learning-to-rank, multi-task learning, and off-policy learning.
Please check my Google Scholar and Github. Contact: Email and Twitter.
News
Our work of Learning-to-Rank with Partitioned Preference is accepted by AISTATS 2021!- Our work of CopulaGNN is accepted by ICLR 2021!
- We are organizing a workshop on Graph Learning Benchmarks (GLB) at the WebConf (WWW) 2021!
Selected Papers
Preprints
- Subgroup Generalization and Fairness of Graph Neural Networks.
Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
Preprint 2021.
[ArXiv] - Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem.
Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
Preprint 2021.
[ArXiv][Code] - SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks.
Weijing Tang*, Jiaqi Ma*, Qiaozhu Mei, Ji Zhu.
Preprint 2020.
[ArXiv][Code]
Conference Publications
- Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model.
Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei.
AISTATS 2021.
[ArXiv][SlidesLive] - CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks.
Jiaqi Ma, Bo Chang, Xuefei Zhang, Qiaozhu Mei.
ICLR 2021.
[ArXiv][OpenReview][Code] - Towards More Practical Adversarial Attacks on Graph Neural Networks.
Jiaqi Ma*, Shuangrui Ding*, Qiaozhu Mei.
NeurIPS 2020.
[ArXiv][Code][SlidesLive] - Off-policy Learning in Two-stage Recommender Systems.
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi.
TheWebConf (WWW) 2020 (with oral presentation).
[Proceedings][Code] - A Flexible Generative Framework for Graph-based Semi-supervised Learning.
Jiaqi Ma*, Weijing Tang*, Ji Zhu, Qiaozhu Mei.
NeurIPS 2019.
[Proceedings][Code] - SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-task Learning.
Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, Ed H. Chi.
AAAI 2019.
[Proceedings] - Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed H. Chi.
KDD 2018 (with oral presentation).
[Proceedings][Video][Presentation] - DeepCas: An End-to-End Predictor of Information Cascades.
Cheng Li, Jiaqi Ma, Xiaoxiao Guo, Qiaozhu Mei.
WWW 2017.
[Proceedings][Code]
(* Equal Contribution)
Work Experience
Jun. 2019 - Aug. 2019
Research Intern, Google Brain. Google Inc., Mountain View, CA, USA.
May 2018 - Aug. 2018
Research Intern, Google Brain. Google Inc., Mountain View, CA, USA.
May 2017 - Sep. 2017
Research Intern, Research & Machine Intelligence. Google Inc., Mountain View, CA, USA.
