Learning cascaded influence under partial monitoring. - Jie Zhang 078; Jiaqi Ma 001; Jie Tang 001 (ASONAM, 2016)

Joint Community and Structural Hole Spanner Detection via Harmonic Modularity. - Lifang He 001; Chun-Ta Lu; Jiaqi Ma 001; Jianping Cao; Linlin Shen; Philip S. Yu (KDD, 2016)

DeepCas: An End-to-end Predictor of Information Cascades. - Cheng Li 012; Jiaqi Ma 001; Xiaoxiao Guo; Qiaozhu Mei (WWW, 2017)

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. - Jiaqi Ma 001; Zhe Zhao 001; Xinyang Yi; Jilin Chen; Lichan Hong; Ed H. Chi (KDD, 2018)

SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning. - Jiaqi Ma 001; Zhe Zhao 001; Jilin Chen; Ang Li; Lichan Hong; Ed H. Chi (AAAI, 2019)

A Flexible Generative Framework for Graph-based Semi-supervised Learning. - Jiaqi Ma 001; Weijing Tang; Ji Zhu; Qiaozhu Mei (NeurIPS, 2019)

Graph Representation Learning via Multi-task Knowledge Distillation. - Jiaqi Ma 001; Qiaozhu Mei (CoRR, 2019)

Towards More Practical Adversarial Attacks on Graph Neural Networks. - Jiaqi Ma 001; Shuangrui Ding; Qiaozhu Mei (NeurIPS, 2020)

Off-policy Learning in Two-stage Recommender Systems. - Jiaqi Ma 001; Zhe Zhao 001; Xinyang Yi; Ji Yang; Minmin Chen; Jiaxi Tang; Lichan Hong; Ed H. Chi (WWW, 2020)

Black-Box Adversarial Attacks on Graph Neural Networks with Limited Node Access. - Jiaqi Ma 001; Shuangrui Ding; Qiaozhu Mei (CoRR, 2020)

Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. - Jiaqi Ma 001; Xinyang Yi; Weijing Tang; Zhe Zhao 001; Lichan Hong; Ed H. Chi; Qiaozhu Mei (AISTATS, 2021)

CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks. - Jiaqi Ma 001; Bo Chang 002; Xuefei Zhang; Qiaozhu Mei (ICLR, 2021)

Subgroup Generalization and Fairness of Graph Neural Networks. - Jiaqi Ma 001; Junwei Deng; Qiaozhu Mei (NeurIPS, 2021)

GReS: Workshop on Graph Neural Networks for Recommendation and Search. - Thibaut Thonet; Stéphane Clinchant; Carlos Lassance; Elvin Isufi; Jiaqi Ma 001; Yutong Xie 007; Jean-Michel Renders; Michael M. Bronstein (RecSys, 2021)

How Much of the Chemical Space Has Been Covered? Measuring and Improving the Variety of Candidate Set in Molecular Generation. - Yutong Xie 007; Ziqiao Xu; Jiaqi Ma 001; Qiaozhu Mei (CoRR, 2021)

Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks. - Jiaqi Ma 001; Xingjian Zhang 002; Hezheng Fan; Jin Huang; Tianyue Li; Ting Wei Li; Yiwen Tu; Chenshu Zhu; Qiaozhu Mei (LoG, 2022)

Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem. - Jiaqi Ma 001; Junwei Deng; Qiaozhu Mei (WSDM, 2022)

Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling. - Jiaqi Ma 001; Xingjian Zhang 002; Qiaozhu Mei (WSDM, 2022)

SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks. - Weijing Tang; Jiaqi Ma 001; Qiaozhu Mei; Ji Zhu (J. Mach. Learn. Res., 2022)

How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules. - Yutong Xie 007; Ziqiao Xu; Jiaqi Ma 001; Qiaozhu Mei (ICLR, 2023)

The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). - Jiaqi Ma 001; Jiong Zhu; Yuxiao Dong; Danai Koutra; Jingrui He; Qiaozhu Mei; Anton Tsitsulin; Xingjian Zhang 002; Marinka Zitnik (KDD, 2023)

A Metadata-Driven Approach to Understand Graph Neural Networks. - Ting Wei Li; Qiaozhu Mei; Jiaqi Ma 001 (NeurIPS, 2023)

Can LLMs Effectively Leverage Graph Structural Information: When and Why. - Jin Huang; Xingjian Zhang 002; Qiaozhu Mei; Jiaqi Ma 001 (CoRR, 2023)

Computational Copyright: Towards A Royalty Model for AI Music Generation Platforms. - Junwei Deng; Jiaqi Ma 001 (CoRR, 2023)

Partition-Based Active Learning for Graph Neural Networks. - Jiaqi Ma 001; Ziqiao Ma 001; Joyce Chai; Qiaozhu Mei (Trans. Mach. Learn. Res., 2023)

PM2.5 forecasting under distribution shift: A graph learning approach. - Yachuan Liu; Jiaqi Ma 001; Paramveer Dhillon; Qiaozhu Mei (AI Open, 2024)

Efficient Ensembles Improve Training Data Attribution. - Junwei Deng; Ting-Wei Li; Shichang Zhang; Jiaqi Ma 001 (CoRR, 2024)

DCA-Bench: A Benchmark for Dataset Curation Agents. - Benhao Huang; Yingzhuo Yu; Jin Huang; Xingjian Zhang 002; Jiaqi Ma 001 (CoRR, 2024)

Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why? - Jin Huang; Xingjian Zhang 002; Qiaozhu Mei; Jiaqi Ma 001 (Trans. Mach. Learn. Res., 2024)

Efficient Estimation of Shortest-Path Distance Distributions to Samples in Graphs. - Alan Zhu 001; Jiaqi Ma 001; Qiaozhu Mei (CoRR, 2025)