Robust Capped Norm Nonnegative Matrix Factorization: Capped Norm NMF. - Hongchang Gao; Feiping Nie 001; Tom Weidong Cai; Heng Huang (CIKM, 2015)

Multi-view Subspace Clustering. - Hongchang Gao; Feiping Nie 001; Xuelong Li 001; Heng Huang (ICCV, 2015)

Anatomical Annotations for Drosophila Gene Expression Patterns via Multi-Dimensional Visual Descriptors Integration: Multi-Dimensional Feature Learning. - Hongchang Gao; Lin Yan 003; Weidong Cai 001; Heng Huang (KDD, 2015)

Identifying Connectome Module Patterns via New Balanced Multi-graph Normalized Cut. - Hongchang Gao; Chengtao Cai; Jingwen Yan; Lin Yan 003; Joaquín Goñi Cortes; Yang Wang; Feiping Nie 001; John D. West; Andrew J. Saykin; Li Shen 001; Heng Huang (MICCAI (2), 2015)

The l2, 1-Norm Stacked Robust Autoencoders for Domain Adaptation. - Wenhao Jiang; Hongchang Gao; Fu-Lai Chung; Heng Huang (AAAI, 2016)

New Robust Clustering Model for Identifying Cancer Genome Landscapes. - Hongchang Gao; Xiaoqian Wang 001; Heng Huang (ICDM, 2016)

Local Centroids Structured Non-Negative Matrix Factorization. - Hongchang Gao; Feiping Nie 001; Heng Huang (AAAI, 2017)

Joint Generative Moment-Matching Network for Learning Structural Latent Code. - Hongchang Gao; Heng Huang (IJCAI, 2018)

Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models. - Hongchang Gao; Heng Huang (IJCAI, 2018)

Deep Attributed Network Embedding. - Hongchang Gao; Heng Huang (IJCAI, 2018)

Self-Paced Network Embedding. - Hongchang Gao; Heng Huang (KDD, 2018)

Attention Convolutional Neural Network for Advertiser-level Click-through Rate Forecasting. - Hongchang Gao; Deguang Kong; Miao Lu; Xiao Bai 002; Jian Yang 002 (WWW, 2018)

Demystifying Dropout. - Hongchang Gao; Jian Pei 001; Heng Huang (ICML, 2019)

Conditional Random Field Enhanced Graph Convolutional Neural Networks. - Hongchang Gao; Jian Pei 001; Heng Huang (KDD, 2019)

ProGAN: Network Embedding via Proximity Generative Adversarial Network. - Hongchang Gao; Jian Pei 001; Heng Huang (KDD, 2019)

Stacked Robust Adaptively Regularized Auto-Regressions for Domain Adaptation. - Wenhao Jiang; Hongchang Gao; Wei Lu 006; Wei Liu 005; Fu-Lai Chung; Heng Huang (IEEE Trans. Knowl. Data Eng., 2019)

Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems? - Hongchang Gao; Heng Huang (ICML, 2020)

Adaptive Serverless Learning. - Hongchang Gao; Heng Huang (CoRR, 2020)

Periodic Stochastic Gradient Descent with Momentum for Decentralized Training. - Hongchang Gao; Heng Huang (CoRR, 2020)

Robust Cumulative Crowdsourcing Framework Using New Incentive Payment Function and Joint Aggregation Model. - Kamran Ghasedi Dizaji; Hongchang Gao; Yanhua Yang; Heng Huang; Cheng Deng (IEEE Trans. Neural Networks Learn. Syst., 2020)

On the Convergence of Communication-Efficient Local SGD for Federated Learning. - Hongchang Gao; An Xu; Heng Huang (AAAI, 2021)

On the Convergence of Stochastic Compositional Gradient Descent Ascent Method. - Hongchang Gao; Xiaoqian Wang 001; Lei Luo; Xinghua Shi (IJCAI, 2021)

Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization. - Hongchang Gao; Hanzi Xu; Slobodan Vucetic (IJCAI, 2021)

PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks. - Junjie Chen 004; Wendy Hui Wang; Hongchang Gao; Xinghua Shi (KDD, 2021)

Fast Training Method for Stochastic Compositional Optimization Problems. - Hongchang Gao; Heng Huang (NeurIPS, 2021)

Faster Stochastic Second Order Method for Large-Scale Machine Learning Models. - Hongchang Gao; Heng Huang (SDM, 2021)

Provable Distributed Stochastic Gradient Descent with Delayed Updates. - Hongchang Gao; Gang Wu 013; Ryan A. Rossi (SDM, 2021)

Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems. - Wenkang Zhan; Gang Wu; Hongchang Gao (AAAI, 2022)

On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum. - Hongchang Gao; Junyi Li; Heng Huang (ICML, 2022)

Gradient-Free Method for Heavily Constrained Nonconvex Optimization. - Wanli Shi; Hongchang Gao; Bin Gu 001 (ICML, 2022)

Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction. - Yanfu Zhang; Hongchang Gao; Jian Pei 001; Heng Huang (WWW, 2022)

On the Convergence of Momentum-Based Algorithms for Federated Stochastic Bilevel Optimization Problems. - Hongchang Gao (CoRR, 2022)

Stochastic Bilevel Distributed Optimization over a Network. - Hongchang Gao; Bin Gu 001; My T. Thai (CoRR, 2022)

Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems. - Hongchang Gao (CoRR, 2022)

Distributed Stochastic Nested Optimization for Emerging Machine Learning Models: Algorithm and Theory. - Hongchang Gao (AAAI, 2023)

On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network. - Hongchang Gao; Bin Gu 001; My T. Thai (AISTATS, 2023)

FedDP: Secure Federated Learning with Differential Privacy for Disease Prediction. - Bin Li; Hongchang Gao; Xinghua Shi (ICCABS, 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 (ICCV, 2023)

Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling. - Jiyao Liu; Xinliang Wei; Xuanzhang Liu; Hongchang Gao; Yu Wang 003 (ICPP, 2023)

Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms. - Yihan Zhang; Meikang Qiu; Hongchang Gao (IJCAI, 2023)

Distributed Optimization for Big Data Analytics: Beyond Minimization. - Hongchang Gao; Xinwen Zhang (KDD, 2023)

Federated Compositional Deep AUC Maximization. - Xinwen Zhang; Yihan Zhang; Tianbao Yang; Richard Souvenir; Hongchang Gao (NeurIPS, 2023)

Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems? - Yihan Zhang; Wenhao Jiang; Feng Zheng; Chiu C. Tan 001; Xinghua Shi; Hongchang Gao (CoRR, 2023)

Stochastic Multi-Level Compositional Optimization Algorithms over Networks with Level-Independent Convergence Rate. - Hongchang Gao (CoRR, 2023)

Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimization. - Hongchang Gao (CoRR, 2023)

On the Communication Complexity of Decentralized Bilevel Optimization. - Yihan Zhang; My T. Thai; Jie Wu 001; Hongchang Gao (CoRR, 2023)

When Decentralized Optimization Meets Federated Learning. - Hongchang Gao; My T. Thai; Jie Wu 001 (IEEE Netw., 2023)

Discriminative Forests Improve Generative Diversity for Generative Adversarial Networks. - Junjie Chen 004; Jiahao Li; Chen Song; Bin Li; Qingcai Chen; Hongchang Gao; Wendy Hui Wang; Zenglin Xu; Xinghua Shi (AAAI, 2024)

Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate. - Hongchang Gao (AISTATS, 2024)

Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning. - Taeuk Jang; Hongchang Gao; Pengyi Shi; Xiaoqian Wang 001 (AISTATS, 2024)

CauchyGCN: Preserving Local Smoothness in Graph Convolutional Networks via a Cauchy-Based Message-Passing Scheme and Clustering Analysis. - Peiyu Liang; Hongchang Gao; Xubin He (ICANN (5), 2024)

A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization. - Hongchang Gao (ICML, 2024)

A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization. - Xinwen Zhang; Ali Payani; Myungjin Lee; Richard Souvenir; Hongchang Gao (ICML, 2024)

Decentralized Stochastic Compositional Gradient Descent for AUPRC Maximization. - Hongchang Gao; Yubin Duan; Yihan Zhang; Jie Wu 001 (SDM, 2024)

Measuring privacy policy compliance in the Alexa ecosystem: In-depth analysis. - Hassan A. Shafei; Hongchang Gao; Chiu C. Tan 001 (Comput. Secur., 2024)

AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation. - Peiyu Liang; Hongchang Gao; Xubin He (CoRR, 2024)

Group Formation and Sampling in Group-Based Hierarchical Federated Learning. - Jiyao Liu; Xuanzhang Liu; Xinliang Wei; Hongchang Gao; Yu Wang 003 (IEEE Trans. Cloud Comput., 2024)

FSLearning: An Efficient Federated Split Learning Framework for Privacy-Preserving Disease Prediction. - Bin Li; Xiaoqian Jiang; Yu-Chun Hsu; Arif Ozgun Harmanci; Hongchang Gao; Xinghua Shi (AIME (1), 2025)