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ORCIDHongchang Gao
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2020 – today
- 2024
[j4]Hassan A. Shafei
, Hongchang Gao, Chiu C. Tan:
Measuring privacy policy compliance in the Alexa ecosystem: In-depth analysis. Comput. Secur. 144: 103963 (2024)- [c41]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: 11338-11345 - [c40]Taeuk Jang, Hongchang Gao, Pengyi Shi, Xiaoqian Wang:
Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning. AISTATS 2024: 28-36 - [c39]Hongchang Gao:
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate. AISTATS 2024: 4402-4410 - [c38]Peiyu Liang, Hongchang Gao, Xubin He:
CauchyGCN: Preserving Local Smoothness in Graph Convolutional Networks via a Cauchy-Based Message-Passing Scheme and Clustering Analysis. ICANN (5) 2024: 48-63 - [c37]Hongchang Gao:
A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization. ICML 2024 - [c36]Xinwen Zhang, Ali Payani, Myungjin Lee, Richard Souvenir, Hongchang Gao:
A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization. ICML 2024 - [c35]Hongchang Gao, Yubin Duan, Yihan Zhang, Jie Wu:
Decentralized Stochastic Compositional Gradient Descent for AUPRC Maximization. SDM 2024: 226-234 - [i14]Peiyu Liang, Hongchang Gao, Xubin He:
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation. CoRR abs/2406.02348 (2024) - [i13]Wanli Shi, Hongchang Gao, Bin Gu:
Gradient-Free Method for Heavily Constrained Nonconvex Optimization. CoRR abs/2409.00459 (2024) - 2023
- [j3]Hongchang Gao, My T. Thai, Jie Wu:
When Decentralized Optimization Meets Federated Learning. IEEE Netw. 37(5): 233-239 (2023) - [c34]Hongchang Gao:
Distributed Stochastic Nested Optimization for Emerging Machine Learning Models: Algorithm and Theory. AAAI 2023: 15437 - [c33]Hongchang Gao, Bin Gu, My T. Thai:
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network. AISTATS 2023: 9238-9281 - [c32]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: 102-111 - [c31]Jiyao Liu, Xinliang Wei, Xuanzhang Liu, Hongchang Gao, Yu Wang:
Group-based Hierarchical Federated Learning: Convergence, Group Formation, and Sampling. ICPP 2023: 264-273 - [c30]Yihan Zhang, Meikang Qiu, Hongchang Gao:
Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms. IJCAI 2023: 4602-4610 - [c29]Hongchang Gao, Xinwen Zhang:
Distributed Optimization for Big Data Analytics: Beyond Minimization. KDD 2023: 5800-5801 - [c28]Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao:
Federated Compositional Deep AUC Maximization. NeurIPS 2023 - [i12]Xinwen Zhang, Yihan Zhang, Tianbao Yang, Richard Souvenir, Hongchang Gao:
Federated Compositional Deep AUC Maximization. CoRR abs/2304.10101 (2023) - [i11]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 abs/2304.11788 (2023) - [i10]Hongchang Gao, My T. Thai, Jie Wu:
When Decentralized Optimization Meets Federated Learning. CoRR abs/2306.02570 (2023) - [i9]Hongchang Gao:
Stochastic Multi-Level Compositional Optimization Algorithms over Networks with Level-Independent Convergence Rate. CoRR abs/2306.03322 (2023) - [i8]Hongchang Gao:
Achieving Linear Speedup in Decentralized Stochastic Compositional Minimax Optimization. CoRR abs/2307.13430 (2023) - [i7]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 abs/2307.14061 (2023) - [i6]Yihan Zhang, My T. Thai, Jie Wu, Hongchang Gao:
On the Communication Complexity of Decentralized Bilevel Optimization. CoRR abs/2311.11342 (2023) - 2022
- [c27]Wenkang Zhan, Gang Wu, Hongchang Gao:
Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems. AAAI 2022: 9006-9013 - [c26]Hongchang Gao, Junyi Li, Heng Huang:
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum. ICML 2022: 7017-7035 - [c25]Wanli Shi, Hongchang Gao, Bin Gu:
Gradient-Free Method for Heavily Constrained Nonconvex Optimization. ICML 2022: 19935-19955 - [c24]Yanfu Zhang, Hongchang Gao, Jian Pei, Heng Huang:
Robust Self-Supervised Structural Graph Neural Network for Social Network Prediction. WWW 2022: 1352-1361 - [i5]Hongchang Gao:
On the Convergence of Momentum-Based Algorithms for Federated Stochastic Bilevel Optimization Problems. CoRR abs/2204.13299 (2022) - [i4]Hongchang Gao, Bin Gu, My T. Thai:
Stochastic Bilevel Distributed Optimization over a Network. CoRR abs/2206.15025 (2022) - [i3]Hongchang Gao:
Decentralized Stochastic Gradient Descent Ascent for Finite-Sum Minimax Problems. CoRR abs/2212.02724 (2022) - 2021
- [c23]Hongchang Gao, An Xu, Heng Huang:
On the Convergence of Communication-Efficient Local SGD for Federated Learning. AAAI 2021: 7510-7518 - [c22]Hongchang Gao, Xiaoqian Wang, Lei Luo, Xinghua Shi:
On the Convergence of Stochastic Compositional Gradient Descent Ascent Method. IJCAI 2021: 2389-2395 - [c21]Hongchang Gao, Hanzi Xu, Slobodan Vucetic:
Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization. IJCAI 2021: 3501-3507 - [c20]Junjie Chen, Wendy Hui Wang, Hongchang Gao, Xinghua Shi:
PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks. KDD 2021: 127-137 - [c19]Hongchang Gao, Heng Huang:
Fast Training Method for Stochastic Compositional Optimization Problems. NeurIPS 2021: 25334-25345 - [c18]Hongchang Gao, Heng Huang:
Faster Stochastic Second Order Method for Large-Scale Machine Learning Models. SDM 2021: 405-413 - [c17]Hongchang Gao, Gang Wu, Ryan A. Rossi:
Provable Distributed Stochastic Gradient Descent with Delayed Updates. SDM 2021: 441-449 - 2020
- [j2]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): 4610-4621 (2020) - [c16]Hongchang Gao, Heng Huang:
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems? ICML 2020: 3377-3386 - [i2]Hongchang Gao, Heng Huang:
Adaptive Serverless Learning. CoRR abs/2008.10422 (2020) - [i1]Hongchang Gao, Heng Huang:
Periodic Stochastic Gradient Descent with Momentum for Decentralized Training. CoRR abs/2008.10435 (2020)
2010 – 2019
- 2019
- [j1]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): 561-574 (2019) - [c15]Hongchang Gao, Jian Pei, Heng Huang:
Demystifying Dropout. ICML 2019: 2112-2121 - [c14]Hongchang Gao, Jian Pei, Heng Huang:
Conditional Random Field Enhanced Graph Convolutional Neural Networks. KDD 2019: 276-284 - [c13]Hongchang Gao, Jian Pei, Heng Huang:
ProGAN: Network Embedding via Proximity Generative Adversarial Network. KDD 2019: 1308-1316 - 2018
- [c12]Hongchang Gao, Heng Huang:
Joint Generative Moment-Matching Network for Learning Structural Latent Code. IJCAI 2018: 2121-2127 - [c11]Hongchang Gao, Heng Huang:
Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models. IJCAI 2018: 2128-2134 - [c10]Hongchang Gao, Heng Huang:
Deep Attributed Network Embedding. IJCAI 2018: 3364-3370 - [c9]Hongchang Gao, Heng Huang:
Self-Paced Network Embedding. KDD 2018: 1406-1415 - [c8]Hongchang Gao, Deguang Kong, Miao Lu, Xiao Bai, Jian Yang:
Attention Convolutional Neural Network for Advertiser-level Click-through Rate Forecasting. WWW 2018: 1855-1864 - 2017
- [c7]Hongchang Gao, Feiping Nie, Heng Huang:
Local Centroids Structured Non-Negative Matrix Factorization. AAAI 2017: 1905-1911 - 2016
- [c6]Wenhao Jiang, Hongchang Gao, Fu-Lai Chung, Heng Huang:
The l2, 1-Norm Stacked Robust Autoencoders for Domain Adaptation. AAAI 2016: 1723-1729 - [c5]Hongchang Gao, Xiaoqian Wang, Heng Huang:
New Robust Clustering Model for Identifying Cancer Genome Landscapes. ICDM 2016: 151-160 - 2015
- [c4]Hongchang Gao, Feiping Nie, Tom Weidong Cai, Heng Huang:
Robust Capped Norm Nonnegative Matrix Factorization: Capped Norm NMF. CIKM 2015: 871-880 - [c3]Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang:
Multi-view Subspace Clustering. ICCV 2015: 4238-4246 - [c2]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: 339-348 - [c1]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: 169-176
Coauthor Index
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