Feihu Huang

Orcid: 0000-0003-0806-6074

Affiliations:
  • Nanjing University of Aeronautics and Astronautics, College of Computer Science and Technology, China
  • University of Pittsburgh, Department of Electrical and Computer Engineering, PA, USA (2018 - 2022)
  • Nanjing University of Aeronautics and Astronautics, College of Computer Science and Technology, China (PhD 2017)


According to our database1, Feihu Huang authored at least 52 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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2021
2022
2023
2024
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Legend:

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Links

Online presence:

On csauthors.net:

Bibliography

2024
Faster Adaptive Decentralized Learning Algorithms.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Adaptive Federated Minimax Optimization with Lower Complexities.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Gradient Descent Ascent for Minimax Problems on Riemannian Manifolds.
IEEE Trans. Pattern Anal. Mach. Intell., July, 2023

Adaptive Mirror Descent Bilevel Optimization.
CoRR, 2023

Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems.
CoRR, 2023

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization.
CoRR, 2023

On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level.
CoRR, 2023

Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems.
CoRR, 2023

FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging.
CoRR, 2023

Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Structural Alignment for Network Pruning through Partial Regularization.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Faster Adaptive Federated Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Faster Stochastic Quasi-Newton Methods.
IEEE Trans. Neural Networks Learn. Syst., 2022

Riemannian gradient methods for stochastic composition problems.
Neural Networks, 2022

Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization.
J. Mach. Learn. Res., 2022

Adaptive Federated Minimax Optimization with Lower complexities.
CoRR, 2022

Faster Adaptive Momentum-Based Federated Methods for Distributed Composition Optimization.
CoRR, 2022

Fast Adaptive Federated Bilevel Optimization.
CoRR, 2022

Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction.
CoRR, 2022

Enhanced Bilevel Optimization via Bregman Distance.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Bregman Gradient Policy Optimization.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Communication-Efficient Adam-Type Algorithms for Distributed Data Mining.
Proceedings of the IEEE International Conference on Data Mining, 2022

Fast Stochastic Recursive Momentum Methods for Imbalanced Data Mining.
Proceedings of the IEEE International Conference on Data Mining, 2022

Disentangled Differentiable Network Pruning.
Proceedings of the Computer Vision - ECCV 2022, 2022

2021
Enhanced Bilevel Optimization via Bregman Distance.
CoRR, 2021

AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization.
CoRR, 2021

BiAdam: Fast Adaptive Bilevel Optimization Methods.
CoRR, 2021

Compositional Federated Learning: Applications in Distributionally Robust Averaging and Meta Learning.
CoRR, 2021

A New Framework for Variance-Reduced Hamiltonian Monte Carlo.
CoRR, 2021

A Faster Decentralized Algorithm for Nonconvex Minimax Problems.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimal Underdamped Langevin MCMC Method.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Network Pruning via Performance Maximization.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Gradient Descent Ascent for Min-Max Problems on Riemannian Manifold.
CoRR, 2020

Accelerated Zeroth-Order Momentum Methods from Mini to Minimax Optimization.
CoRR, 2020

Accelerated Stochastic Gradient-free and Projection-free Methods.
Proceedings of the 37th International Conference on Machine Learning, 2020

Momentum-Based Policy Gradient Methods.
Proceedings of the 37th International Conference on Machine Learning, 2020

Discrete Model Compression With Resource Constraint for Deep Neural Networks.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity.
CoRR, 2019

Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization.
Proceedings of the 36th International Conference on Machine Learning, 2019

Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Joint Estimation of Multiple Conditional Gaussian Graphical Models.
IEEE Trans. Neural Networks Learn. Syst., 2018

Learning Dynamic Conditional Gaussian Graphical Models.
IEEE Trans. Knowl. Data Eng., 2018

Mini-Batch Stochastic ADMMs for Nonconvex Nonsmooth Optimization.
CoRR, 2018

2015
Joint Learning of Multiple Sparse Matrix Gaussian Graphical Models.
IEEE Trans. Neural Networks Learn. Syst., 2015


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