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Feihu Huang 0001
Person information
- affiliation: Nanjing University of Aeronautics and Astronautics, College of Computer Science and Technology, China
- affiliation (2018 - 2022): University of Pittsburgh, Department of Electrical and Computer Engineering, PA, USA
- affiliation (PhD 2017): Nanjing University of Aeronautics and Astronautics, College of Computer Science and Technology, China
Other persons with the same name
- Feihu Huang 0002 — Civil Aviation Flight University of China, School of Science, Guanghan, China (and 1 more)
- Feihu Huang 0003 — Wuhan University of Science and Technology, College of Computer Science, China
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2020 – today
- 2024
- [c26]Feihu Huang, Xinrui Wang, Junyi Li, Songcan Chen:
Adaptive Federated Minimax Optimization with Lower Complexities. AISTATS 2024: 4663-4671 - [c25]Shangqian Gao, Yanfu Zhang, Feihu Huang, Heng Huang:
BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks. CVPR 2024: 16090-16100 - [c24]Junyi Li, Feihu Huang, Heng Huang:
FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization. ICLR 2024 - [c23]Feihu Huang:
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization. ICML 2024 - [c22]Feihu Huang, Jianyu Zhao:
Faster Adaptive Decentralized Learning Algorithms. ICML 2024 - [i30]Feihu Huang:
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization. CoRR abs/2407.17823 (2024) - [i29]Feihu Huang, Jianyu Zhao:
Faster Adaptive Decentralized Learning Algorithms. CoRR abs/2408.09775 (2024) - 2023
- [j7]Feihu Huang, Shangqian Gao:
Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds. IEEE Trans. Pattern Anal. Mach. Intell. 45(7): 8466-8476 (2023) - [c21]Xidong Wu, Feihu Huang, Zhengmian Hu, Heng Huang:
Faster Adaptive Federated Learning. AAAI 2023: 10379-10387 - [c20]Feihu Huang, Xidong Wu, Zhengmian Hu:
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization. AISTATS 2023: 2365-2389 - [c19]Shangqian Gao, Zeyu Zhang, Yanfu Zhang, Feihu Huang, Heng Huang:
Structural Alignment for Network Pruning through Partial Regularization. ICCV 2023: 17356-17366 - [c18]Junyi Li, Feihu Huang, Heng Huang:
Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems. NeurIPS 2023 - [i28]Junyi Li, Feihu Huang, Heng Huang:
FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging. CoRR abs/2302.06103 (2023) - [i27]Junyi Li, Feihu Huang, Heng Huang:
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems. CoRR abs/2302.06701 (2023) - [i26]Feihu Huang:
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level. CoRR abs/2303.03944 (2023) - [i25]Feihu Huang:
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization. CoRR abs/2303.03984 (2023) - [i24]Feihu Huang, Songcan Chen:
Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems. CoRR abs/2304.10902 (2023) - [i23]Feihu Huang:
Adaptive Mirror Descent Bilevel Optimization. CoRR abs/2311.04520 (2023) - 2022
- [j6]Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang:
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization. J. Mach. Learn. Res. 23: 36:1-36:70 (2022) - [j5]Feihu Huang, Shangqian Gao:
Riemannian gradient methods for stochastic composition problems. Neural Networks 153: 224-234 (2022) - [j4]Qingsong Zhang, Feihu Huang, Cheng Deng, Heng Huang:
Faster Stochastic Quasi-Newton Methods. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4388-4397 (2022) - [c17]Shangqian Gao, Feihu Huang, Yanfu Zhang, Heng Huang:
Disentangled Differentiable Network Pruning. ECCV (11) 2022: 328-345 - [c16]Xidong Wu, Feihu Huang, Heng Huang:
Fast Stochastic Recursive Momentum Methods for Imbalanced Data Mining. ICDM 2022: 578-587 - [c15]Wenhan Xian, Feihu Huang, Heng Huang:
Communication-Efficient Adam-Type Algorithms for Distributed Data Mining. ICDM 2022: 1245-1250 - [c14]Feihu Huang, Shangqian Gao, Heng Huang:
Bregman Gradient Policy Optimization. ICLR 2022 - [c13]Feihu Huang, Junyi Li, Shangqian Gao, Heng Huang:
Enhanced Bilevel Optimization via Bregman Distance. NeurIPS 2022 - [i22]Junyi Li, Feihu Huang, Heng Huang:
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction. CoRR abs/2205.01608 (2022) - [i21]Wenhan Xian, Feihu Huang, Heng Huang:
Communication-Efficient Adam-Type Algorithms for Distributed Data Mining. CoRR abs/2210.07454 (2022) - [i20]Feihu Huang:
Fast Adaptive Federated Bilevel Optimization. CoRR abs/2211.01122 (2022) - [i19]Feihu Huang:
Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization. CoRR abs/2211.01883 (2022) - [i18]Feihu Huang:
Adaptive Federated Minimax Optimization with Lower complexities. CoRR abs/2211.07303 (2022) - [i17]Xidong Wu, Feihu Huang, Zhengmian Hu, Heng Huang:
Faster Adaptive Federated Learning. CoRR abs/2212.00974 (2022) - 2021
- [c12]Wenhan Xian, Feihu Huang, Heng Huang:
Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning. AAAI 2021: 10405-10413 - [c11]Shangqian Gao, Feihu Huang, Weidong Cai, Heng Huang:
Network Pruning via Performance Maximization. CVPR 2021: 9270-9280 - [c10]Feihu Huang, Junyi Li, Heng Huang:
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients. NeurIPS 2021: 9074-9085 - [c9]Feihu Huang, Xidong Wu, Heng Huang:
Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems. NeurIPS 2021: 10431-10443 - [c8]Zhengmian Hu, Feihu Huang, Heng Huang:
Optimal Underdamped Langevin MCMC Method. NeurIPS 2021: 19363-19374 - [c7]Wenhan Xian, Feihu Huang, Yanfu Zhang, Heng Huang:
A Faster Decentralized Algorithm for Nonconvex Minimax Problems. NeurIPS 2021: 25865-25877 - [i16]Zhengmian Hu, Feihu Huang, Heng Huang:
A New Framework for Variance-Reduced Hamiltonian Monte Carlo. CoRR abs/2102.04613 (2021) - [i15]Feihu Huang, Junyi Li, Heng Huang:
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients. CoRR abs/2106.08208 (2021) - [i14]Feihu Huang, Junyi Li, Heng Huang:
Compositional Federated Learning: Applications in Distributionally Robust Averaging and Meta Learning. CoRR abs/2106.11264 (2021) - [i13]Feihu Huang, Heng Huang:
BiAdam: Fast Adaptive Bilevel Optimization Methods. CoRR abs/2106.11396 (2021) - [i12]Feihu Huang, Shangqian Gao, Heng Huang:
Bregman Gradient Policy Optimization. CoRR abs/2106.12112 (2021) - [i11]Feihu Huang, Heng Huang:
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization. CoRR abs/2106.16101 (2021) - [i10]Feihu Huang, Heng Huang:
Enhanced Bilevel Optimization via Bregman Distance. CoRR abs/2107.12301 (2021) - 2020
- [c6]Shangqian Gao, Feihu Huang, Jian Pei, Heng Huang:
Discrete Model Compression With Resource Constraint for Deep Neural Networks. CVPR 2020: 1896-1905 - [c5]Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang:
Momentum-Based Policy Gradient Methods. ICML 2020: 4422-4433 - [c4]Feihu Huang, Lue Tao, Songcan Chen:
Accelerated Stochastic Gradient-free and Projection-free Methods. ICML 2020: 4519-4530 - [i9]Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang:
Momentum-Based Policy Gradient Methods. CoRR abs/2007.06680 (2020) - [i8]Feihu Huang, Lue Tao, Songcan Chen:
Accelerated Stochastic Gradient-free and Projection-free Methods. CoRR abs/2007.12625 (2020) - [i7]Feihu Huang, Songcan Chen, Heng Huang:
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization. CoRR abs/2008.01296 (2020) - [i6]Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang:
Accelerated Zeroth-Order Momentum Methods from Mini to Minimax Optimization. CoRR abs/2008.08170 (2020) - [i5]Feihu Huang, Shangqian Gao, Heng Huang:
Gradient Descent Ascent for Min-Max Problems on Riemannian Manifold. CoRR abs/2010.06097 (2020)
2010 – 2019
- 2019
- [c3]Feihu Huang, Bin Gu, Zhouyuan Huo, Songcan Chen, Heng Huang:
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization. AAAI 2019: 1503-1510 - [c2]Feihu Huang, Songcan Chen, Heng Huang:
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization. ICML 2019: 2839-2848 - [c1]Feihu Huang, Shangqian Gao, Songcan Chen, Heng Huang:
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization. IJCAI 2019: 2549-2555 - [i4]Feihu Huang, Bin Gu, Zhouyuan Huo, Songcan Chen, Heng Huang:
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization. CoRR abs/1902.06158 (2019) - [i3]Feihu Huang, Shangqian Gao, Songcan Chen, Heng Huang:
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization. CoRR abs/1905.12729 (2019) - [i2]Feihu Huang, Shangqian Gao, Jian Pei, Heng Huang:
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity. CoRR abs/1907.13463 (2019) - 2018
- [j3]Feihu Huang, Songcan Chen:
Learning Dynamic Conditional Gaussian Graphical Models. IEEE Trans. Knowl. Data Eng. 30(4): 703-716 (2018) - [j2]Feihu Huang, Songcan Chen, Sheng-Jun Huang:
Joint Estimation of Multiple Conditional Gaussian Graphical Models. IEEE Trans. Neural Networks Learn. Syst. 29(7): 3034-3046 (2018) - [i1]Feihu Huang, Songcan Chen:
Mini-Batch Stochastic ADMMs for Nonconvex Nonsmooth Optimization. CoRR abs/1802.03284 (2018) - 2015
- [j1]Feihu Huang, Songcan Chen:
Joint Learning of Multiple Sparse Matrix Gaussian Graphical Models. IEEE Trans. Neural Networks Learn. Syst. 26(11): 2606-2620 (2015)
Coauthor Index
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last updated on 2024-10-22 20:14 CEST by the dblp team
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