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Publications

(# indicates advisees, * indicates equal contributions.)

Tutorials

  • Federated Stochastic Compositional and Bilevel Optimization.
    Hongchang Gao, Xinwen Zhang#.
    In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI-25).

  • Distributed Stochastic Nested Optimization for Emerging Machine Learning Models.
    Hongchang Gao.
    In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI 2024).

  • Distributed Optimization for Big Data Analytics: Beyond Minimization.
    Hongchang Gao, Xinwen Zhang#.
    In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023).

Refereed Conference Publications

  • Federated Stochastic Bilevel Optimization with Fully First-Order Gradients.
    Yihan Zhang#, Rohit Dhaipule, Chiu C. Tan, Haibin Ling, Hongchang Gao.
    In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI-25).

  • A Federated Stochastic Multi-Level Compositional Minimax Algorithm for Deep AUC Maximization.
    Xinwen Zhang#, Ali Payani, Myungjin Lee, Richard Souvenir, Hongchang Gao.
    In Proceedings of the 41st International Conference on Machine Learning (ICML 2024).

  • A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization.
    Hongchang Gao.
    In Proceedings of the 41st International Conference on Machine Learning (ICML 2024).

  • Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate.
    Hongchang Gao.
    In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024).

  • Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning.
    Taeuk Jang, Hongchang Gao, Pengyi Shi, Xiaoqian Wang.
    In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024).

  • Decentralized Stochastic Compositional Gradient Descent for AUPRC Maximization.
    Hongchang Gao, Yubin Duan, Yihan Zhang#, Jie Wu.
    In Proceedings of SIAM International Conference on Data Mining (SDM 2024).

  • Discriminative Forests Improve Generative Diversity for Generative Adversarial Networks.
    Junjie Chen, Jiahao Li, Song Chen, Bin Li, Qingcai Chen, Hongchang Gao, Hui Wang, Zenglin Xu, Mindy Shi.
    In Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI 2024).

  • Federated Compositional Deep AUC Maximization.
    Xinwen Zhang*#, Yihan Zhang*#, Tianbao Yang, Richard Souvenir, Hongchang Gao.
    In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

  • Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models.
    Dong Lu, Zhiqiang Wang, Teng Wang, Weili Guan, Hongchang Gao, Feng Zheng.
    In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2023).

  • Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling.
    Jiyao Liu, Xinliang Wei, Xuanzhang Liu, Hongchang Gao, Yu Wang.
    In Proceedings of the 52nd International Conference on Parallel Processing (ICPP 2023).

  • Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms.
    Yihan Zhang#, Meikang Qiu, Hongchang Gao.
    In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023).

  • On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network.
    Hongchang Gao, Bin Gu, My T. Thai.
    In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023).

  • Distributed Stochastic Nested Optimization for Emerging Machine Learning Models: Algorithm and Theory.
    Hongchang Gao.
    AAAI 2023 New Faculty Highlights.

  • On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum.
    Hongchang Gao, Junyi Li, Heng Huang.
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022).

  • Gradient-Free Method for Heavily Constrained Nonconvex Optimization.
    Wanli Shi, Hongchang Gao, Bin Gu.
    In Proceedings of the 39th International Conference on Machine Learning (ICML 2022).

  • Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction.
    Yanfu Zhang, Hongchang Gao, Jian Pei, Heng Huang.
    In Proceedings of The Web Conference (WWW 2022).

  • Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems.
    Wenkang Zhan, Gang Wu, Hongchang Gao.
    In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022).
    [Supplement]

  • Fast Training Method for Stochastic Compositional Optimization Problems.
    Hongchang Gao, Heng Huang.
    In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).

  • PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks.
    Junjie Chen, Hui Wang, Hongchang Gao, Mindy Shi.
    In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021).

  • Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization.
    Hongchang Gao, Hanzi Xu, Slobodan Vucetic.
    In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).
    [Supplement]

  • On the Convergence of Stochastic Compositional Gradient Descent Ascent Method.
    Hongchang Gao, Xiaoqian Wang, Lei Luo, Mindy Shi.
    In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021).
    [Supplement]

  • On the Convergence of Communication-Efficient Local SGD for Federated Learning.
    Hongchang Gao, An Xu, Heng Huang.
    In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021).
    [Supplement]

  • Provable Distributed Stochastic Gradient Descent with Delayed Updates.
    Hongchang Gao, Gang Wu, Ryan Rossi.
    In Proceedings of SIAM International Conference on Data Mining (SDM 2021).
    [Supplement]

  • Faster Stochastic Second Order Method for Large-scale Machine Learning Models.
    Hongchang Gao, Heng Huang.
    In Proceedings of SIAM International Conference on Data Mining (SDM 2021).

2020 and Before

  • Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems?
    Hongchang Gao, Heng Huang.
    In Proceedings of the 37th International Conference on Machine Learning (ICML 2020).

  • Demystifying Dropout.
    Hongchang Gao, Jian Pei, Heng Huang.
    In Proceedings of the 36th International Conference on Machine Learning (ICML 2019).

  • Conditional Random Field Enhanced Graph Convolutional Neural Networks.
    Hongchang Gao, Jian Pei, Heng Huang.
    In Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019).

  • ProGAN: Network Embedding via Proximity Generative Adversarial Network.
    Hongchang Gao, Jian Pei, Heng Huang.
    In Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019).

  • Self-Paced Network Embedding.
    Hongchang Gao, Heng Huang.
    In Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018).

  • Deep Attributed Network Embedding.
    Hongchang Gao, Heng Huang.
    In Proceedings of the 27th International Joint Conferences on Artificial Intelligence (IJCAI 2018).

  • Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models.
    Hongchang Gao, Heng Huang.
    In Proceedings of the 27th International Joint Conferences on Artificial Intelligence (IJCAI 2018).

  • Joint Generative Moment-Matching Network for Learning Structural Latent Code.
    Hongchang Gao, Heng Huang.
    In Proceedings of the 27th International Joint Conferences on Artificial Intelligence (IJCAI 2018).

  • Attention Convolutional Neural Network for Advertiser-level Click-through Rate Forecasting.
    Hongchang Gao, Deguang Kong, Miao Lu, Xiao Bai, Jian Yang.
    In Proceedings of the 29th World Wide Web Conference (WWW 2018).

  • Local Centroids Structured Non-Negative Matrix Factorization.
    Hongchang Gao, Feiping Nie, Heng Huang.
    In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017).

  • The l2, 1-Norm Stacked Robust Autoencoders for Domain Adaptation.
    Wenhao Jiang, Hongchang Gao, Fu-Lai Chung, Heng Huang.
    In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016).

  • New Robust Clustering Model for Identifying Cancer Genome Landscapes.
    Hongchang Gao*, Xiaoqian Wang*, Heng Huang.
    In Proceedings of the IEEE International Conference on Data Mining (ICDM 2016).

  • Robust Capped Norm Nonnegative Matrix Factorization: Capped Norm NMF.
    Hongchang Gao, Feiping Nie, Tom Weidong Cai, Heng Huang.
    In Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015).

  • Multi-view Subspace Clustering.
    Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang.
    In Proceedings of the IEEE International Conference on Computer Vision (ICCV 2015).

  • Anatomical Annotations for Drosophila Gene Expression Patterns via Multi-Dimensional Visual Descriptors Integration: Multi-Dimensional Feature Learning.
    Hongchang Gao, Lin Yan, Weidong Cai, Heng Huang.
    In Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015).

  • Identifying Connectome Module Patterns via New Balanced Multi-graph Normalized Cut.
    Hongchang Gao, Chengtao Cai, Jingwen Yan, Lin Yan, Joaquín Goñi Cortes, Yang Wang, Feiping Nie, John D. West, Andrew J. Saykin, Li Shen, Heng Huang.
    In Proceedings of the 18th International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI 2015).

Refereed Journal Publications

  • Group Formation and Sampling in Group-Based Hierarchical Federated Learning.
    Jiyao Liu, Xuanzhang Liu, Xinliang Wei, Hongchang Gao, Yu Wang.
    IEEE Transactions on Cloud Computing, 2024.

  • Measuring privacy policy compliance in the Alexa ecosystem: In-depth analysis.
    Hassan A Shafei, Hongchang Gao, Chiu C. Tan.
    Computers & Security, 2024.

  • When Decentralized Optimization Meets Federated Learning.
    Hongchang Gao, My T. Thai, Jie Wu.
    IEEE Network Magazine, 2023.

  • Robust Cumulative Crowdsourcing Framework Using New Incentive Payment Function and Joint Aggregation Model.
    Kamran Ghasedi Dizaji, Hongchang Gao, Cheng Deng, Yanhua Yang, Heng Huang.
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2019).

  • Stacked Robust Adaptively Regularized Auto-regressions for Domain Adaptation.
    Wenhao Jiang, Hongchang Gao, Wei Lu, Wei Liu, Fu-Lai Chung, Heng Huang.
    IEEE Transactions on Knowledge and Data Engineering (TKDE 2018).

Preprints