Hongchang Gao

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 51

Top Topics

Stochastic Gradient Descent

Negative Matrix Factorization

Top Venues

Publications

Hassan A. Shafei, Hongchang Gao, Chiu C. Tan
Measuring privacy policy compliance in the Alexa ecosystem: In-depth analysis. Comput. Secur. 144 (2024)
Hongchang Gao, Yubin Duan, Yihan Zhang, Jie Wu
Decentralized Stochastic Compositional Gradient Descent for AUPRC Maximization. SDM (2024)
Peiyu Liang, Hongchang Gao, Xubin He
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation. CoRR (2024)
Hongchang Gao
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate. AISTATS (2024)
Junjie Chen, Jiahao Li, Chen Song, Bin Li, Qingcai Chen, Hongchang Gao, Wendy Hui Wang, Zenglin Xu, Xinghua Shi
Discriminative Forests Improve Generative Diversity for Generative Adversarial Networks. AAAI (2024)
Taeuk Jang, Hongchang Gao, Pengyi Shi, Xiaoqian Wang
Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning. AISTATS (2024)
Hongchang Gao
Distributed Stochastic Nested Optimization for Emerging Machine Learning Models: Algorithm and Theory. AAAI (2023)
Yihan Zhang, Meikang Qiu, Hongchang Gao
Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms. IJCAI (2023)
Hongchang Gao, Xinwen Zhang
Distributed Optimization for Big Data Analytics: Beyond Minimization. KDD (2023)
Dong Lu, Zhiqiang Wang, Teng Wang, Weili Guan, Hongchang Gao, Feng Zheng
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models. CoRR (2023)
Jiyao Liu, Xinliang Wei, Xuanzhang Liu, Hongchang Gao, Yu Wang
Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling. ICPP (2023)
Hongchang Gao
Stochastic Multi-Level Compositional Optimization Algorithms over Networks with Level-Independent Convergence Rate. CoRR (2023)
Hongchang Gao, Bin Gu, My T. Thai
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network. AISTATS (2023)
Dong Lu, Zhiqiang Wang, Teng Wang, Weili Guan, Hongchang Gao, Feng Zheng
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models. ICCV (2023)
Hongchang Gao, My T. Thai, Jie Wu
When Decentralized Optimization Meets Federated Learning. CoRR (2023)
Yihan Zhang, My T. Thai, Jie Wu, Hongchang Gao
On the Communication Complexity of Decentralized Bilevel Optimization. CoRR (2023)
Yihan Zhang, Wenhao Jiang, Feng Zheng, Chiu C. Tan, Xinghua Shi, Hongchang Gao
Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems? CoRR (2023)
Hongchang Gao
Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimization. CoRR (2023)
Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
Federated Compositional Deep AUC Maximization. NeurIPS (2023)
Hongchang Gao, My T. Thai, Jie Wu
When Decentralized Optimization Meets Federated Learning. IEEE Netw. 37 (5) (2023)
Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao
Federated Compositional Deep AUC Maximization. CoRR (2023)
Hongchang Gao
On the Convergence of Momentum-Based Algorithms for Federated Stochastic Bilevel Optimization Problems. CoRR (2022)
Hongchang Gao, Junyi Li, Heng Huang
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum. ICML (2022)
Wenkang Zhan, Gang Wu, Hongchang Gao
Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems. AAAI (2022)
Yanfu Zhang, Hongchang Gao, Jian Pei, Heng Huang
Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction. WWW (2022)
Hongchang Gao, Bin Gu, My T. Thai
Stochastic Bilevel Distributed Optimization over a Network. CoRR (2022)
Hongchang Gao
Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems. CoRR (2022)
Wanli Shi, Hongchang Gao, Bin Gu
Gradient-Free Method for Heavily Constrained Nonconvex Optimization. ICML (2022)
Hongchang Gao, Gang Wu, Ryan A. Rossi
Provable Distributed Stochastic Gradient Descent with Delayed Updates. SDM (2021)
Hongchang Gao, Heng Huang
Faster Stochastic Second Order Method for Large-Scale Machine Learning Models. SDM (2021)
Junjie Chen, Wendy Hui Wang, Hongchang Gao, Xinghua Shi
PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks. KDD (2021)
Hongchang Gao, An Xu, Heng Huang
On the Convergence of Communication-Efficient Local SGD for Federated Learning. AAAI (2021)
Hongchang Gao, Xiaoqian Wang, Lei Luo, Xinghua Shi
On the Convergence of Stochastic Compositional Gradient Descent Ascent Method. IJCAI (2021)
Hongchang Gao, Heng Huang
Fast Training Method for Stochastic Compositional Optimization Problems. NeurIPS (2021)
Hongchang Gao, Hanzi Xu, Slobodan Vucetic
Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization. IJCAI (2021)
Hongchang Gao, Heng Huang
Adaptive Serverless Learning. CoRR (2020)
Hongchang Gao, Heng Huang
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems? ICML (2020)
Kamran Ghasedi Dizaji, Hongchang Gao, Yanhua Yang, Heng Huang, Cheng Deng
Robust Cumulative Crowdsourcing Framework Using New Incentive Payment Function and Joint Aggregation Model. IEEE Trans. Neural Networks Learn. Syst. 31 (11) (2020)
Hongchang Gao, Heng Huang
Periodic Stochastic Gradient Descent with Momentum for Decentralized Training. CoRR (2020)
Hongchang Gao, Jian Pei, Heng Huang
ProGAN: Network Embedding via Proximity Generative Adversarial Network. KDD (2019)
Hongchang Gao, Jian Pei, Heng Huang
Demystifying Dropout. ICML (2019)
Wenhao Jiang, Hongchang Gao, Wei Lu, Wei Liu, Fu-Lai Chung, Heng Huang
Stacked Robust Adaptively Regularized Auto-Regressions for Domain Adaptation. IEEE Trans. Knowl. Data Eng. 31 (3) (2019)
Hongchang Gao, Jian Pei, Heng Huang
Conditional Random Field Enhanced Graph Convolutional Neural Networks. KDD (2019)
Hongchang Gao, Deguang Kong, Miao Lu, Xiao Bai, Jian Yang
Attention Convolutional Neural Network for Advertiser-level Click-through Rate Forecasting. WWW (2018)
Hongchang Gao, Heng Huang
Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models. IJCAI (2018)
Hongchang Gao, Heng Huang
Deep Attributed Network Embedding. IJCAI (2018)
Hongchang Gao, Heng Huang
Self-Paced Network Embedding. KDD (2018)
Hongchang Gao, Heng Huang
Joint Generative Moment-Matching Network for Learning Structural Latent Code. IJCAI (2018)
Hongchang Gao, Feiping Nie, Heng Huang
Local Centroids Structured Non-Negative Matrix Factorization. AAAI (2017)
Hongchang Gao, Xiaoqian Wang, Heng Huang
New Robust Clustering Model for Identifying Cancer Genome Landscapes. ICDM (2016)
Wenhao Jiang, Hongchang Gao, Fu-Lai Chung, Heng Huang
The l2, 1-Norm Stacked Robust Autoencoders for Domain Adaptation. AAAI (2016)
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
Identifying Connectome Module Patterns via New Balanced Multi-graph Normalized Cut. MICCAI (2) (2015)
Hongchang Gao, Feiping Nie, Tom Weidong Cai, Heng Huang
Robust Capped Norm Nonnegative Matrix Factorization: Capped Norm NMF. CIKM (2015)
Hongchang Gao, Lin Yan, Weidong Cai, Heng Huang
Anatomical Annotations for Drosophila Gene Expression Patterns via Multi-Dimensional Visual Descriptors Integration: Multi-Dimensional Feature Learning. KDD (2015)
Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang
Multi-view Subspace Clustering. ICCV (2015)