publications

preprints and publications in reverse chronological order. generated by jekyll-scholar.

Preprints

  1. Characterizing and Mitigating Oversquashing in Dynamic Graph Neural Networks via Temporal Curvature
    Yongjian Zhong, Yijie Zhang, Sourya Roy, and Bijaya Adhikari
    In submission
  2. Boosting Continuous-time Dynamic Graph Learning via Implicit Neural Embeddings
    Yongjian Zhong, and Bijaya Adhikari
    In submission
  3. Dissecting Long-range Dependency in Graph Implicit Models
    Yongjian Zhong, and Bijaya Adhikari
    In submission

Publications

2025

  1. Implicit Hypergraph Neural Network
    Akash Choudhuri, Yongjian Zhong, and Bijaya Adhikari
    In IEEE International Conference on Big Data (BigData), 2025
  2. Conformal Edge-Weight Prediction in Latent Space
    Akash Choudhuri, Yongjian Zhong, Mehrdad Moharrami, Christine Klymko, Mark Heimann, and 2 more authors
    In SIAM International Conference on Data Mining (SDM), 2025
  3. Implicit Subgraph Neural Network
    Yongjian Zhong, Liao Zhu, Hieu Vu, and Bijaya Adhikari
    In Forty-second International Conference on Machine Learning, 2025

2024

  1. End-to-End Risk-Aware Reinforcement Learning to Detect Asymptomatic Cases in Healthcare Facilities
    Yongjian Zhong, Weiyu Huang, and Bijaya Adhikari
    In IEEE 12th International Conference on Healthcare Informatics (ICHI), 2024
  2. Efficient and effective implicit dynamic graph neural network
    Yongjian Zhong, Hieu Vu, Tianbao Yang, and Bijaya Adhikari
    In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

  1. SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data
    Bang An, Xun Zhou, Yongjian Zhong, and Tianbao Yang
    Advances in Neural Information Processing Systems, 2023

2022

  1. Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence
    Zhihao Qiu, Quanqi Hu, Yongjian Zhong, Lijun Zhang, and Tianbao Yang
    In International Conference on Machine Learning (ICML), 2022
  2. Multi-block min-max bilevel optimization with applications in multi-task deep auc maximization
    Quanqi Hu, Yongjian Zhong, and Tianbao Yang
    Advances in Neural Information Processing Systems (NeurIPS), 2022

2021

  1. Learning to reweight examples in multi-label classification
    Yongjian Zhong, Chang Xu, Bo Du, and Lefei Zhang
    Neural Networks, 2021

2020

  1. An innovative multi-label learning based algorithm for city data computing
    Mengqing Mei, Yongjian Zhong, Fazhi He, and Chang Xu
    GeoInformatica, 2020

2018

  1. Independent feature and label components for multi-label classification
    Yongjian Zhong, Chang Xu, Bo Du, and Lefei Zhang
    In 2018 IEEE International Conference on Data Mining (ICDM), 2018
    Long paper