About Me
I am a final 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 generally interested in understanding and improving machine learning for complex real-world data (e.g., graphs, rankings, partially observed data), under different contexts (e.g., distribution shift), in terms of various metrics (e.g., accuracy, efficiency, robustness, fairness). I’m particularly interested in research problems motivated by human-related applications. Some “buzzwords” that my existing research is relevant to include graph machine learning, trustworthy machine learning, multi-task learning, and recommender systems.
Please check my Google Scholar and Github. Contact: Email and Twitter.
Pronunciation of my first name: Jia-Chi.
News
We are organizing the 2nd Workshop on Graph Learning Benchmarks (GLB) at the WebConf (WWW) 2022! CfP is online.- Take a look at my blog post on the GLB 2021 workshop we organized earlier this year.
- Two papers accepted by WSDM 2022!
- One paper on Generalization and Fairness of GNN accepted as Spotlight by NeurIPS 2021!
- One paper (SODEN) accepted by JMLR!
- One paper on Learning-to-Rank with Partitioned Preference accepted by AISTATS 2021!
- One paper (CopulaGNN) accepted by ICLR 2021!
Selected Papers
Conference Publications
- Fast Learning of MNL Model From General Partial Rankings with Application to Network Formation Modeling.
Jiaqi Ma*, Xingjian Zhang*, Qiaozhu Mei.
WSDM 2022.
[ArXiv][Code] - Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem.
Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
WSDM 2022.
[ArXiv][Code] - Subgroup Generalization and Fairness of Graph Neural Networks.
Jiaqi Ma*, Junwei Deng*, Qiaozhu Mei.
NeurIPS 2021. (Spotlight, top 3%)
[ArXiv][Code] - 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][SlidesLive] - 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]
Journal Publications
- SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks.
Weijing Tang*, Jiaqi Ma*, Qiaozhu Mei, Ji Zhu.
Accepted by JMLR.
[ArXiv][Code] - Semi-Supervised Joint Learning for Longitudinal Clinical Events Classification Using Neural Network Models.
Weijing Tang, Jiaqi Ma, Akbar K. Waljee, Ji Zhu.
Stat. 2020.
[Paper]
(* 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.
