Feihu Huang
31 papers · 2019–2025 · 10 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (10) π Academic Marathon (6) π Interdisciplinary Bridge π§ Keyword Pioneer π£ Hot Topic Early Bird
π
Cross-Pollinator
(11)
πΊοΈ
Taxonomy Completionist
(32)
π
Conference Polyglot
(10)
π€
Dynamic Duo
(21)
π
Grand Slam
π¬
Deep Specialist
(16)
π
Century Club
(31)
π
Conference Pioneer
ποΈ
Keyword Collector
(84)
β‘
Prolific Year
(6)
π₯
Unstoppable
(7)
Conferences
ICML (6)
NIPS (6)
AAAI (5)
AISTATS (3)
CVPR (3)
ICLR (3)
IJCAI (2)
ECCV (1)
ICCV (1)
JMLR (1)
Top co-authors
Keywords
variance reduction
(11)
stochastic optimization
(6)
minimax optimization
(6)
nonconvex optimization
(6)
gradient complexity
(5)
federated learning
(4)
channel pruning
(4)
zeroth-order optimization
(3)
stochastic gradient
(3)
bilevel optimization
(3)
model compression
(3)
adaptive learning rate
(3)
communication efficiency
(3)
convolutional neural network
(3)
network pruning
(2)
sample complexity
(2)
alternating direction method of multiplier
(2)
momentum method
(2)
convergence rate
(2)
decentralized optimization
(2)
Papers
Faster Double Adaptive Gradient Methods
AAAI 2025
Generalized Smooth Bilevel Optimization with Nonconvex Lower-Level
ICML 2025
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
AISTATS 2025
Escaping Saddle Point Efficiently in Minimax and Bilevel Optimizations
IJCAI 2025
Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update
AAAI 2025
Optimal Hessian/Jacobian-Free Nonconvex-PL Bilevel Optimization
ICML 2024
BilevelPruning: Unified Dynamic and Static Channel Pruning for Convolutional Neural Networks
CVPR 2024
Adaptive Federated Minimax Optimization with Lower Complexities
AISTATS 2024
FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization
ICLR 2024
Faster Adaptive Decentralized Learning Algorithms
ICML 2024
Structural Alignment for Network Pruning through Partial Regularization
ICCV 2023
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting
ICLR 2023
Faster Adaptive Federated Learning
AAAI 2023
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization
AISTATS 2023
Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems
NIPS 2023
Bregman Gradient Policy Optimization
ICLR 2022
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization
JMLR 2022
Disentangled Differentiable Network Pruning
ECCV 2022
Enhanced Bilevel Optimization via Bregman Distance
NIPS 2022
Network Pruning via Performance Maximization
CVPR 2021
Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems
NIPS 2021
Optimal Underdamped Langevin MCMC Method
NIPS 2021
A Faster Decentralized Algorithm for Nonconvex Minimax Problems
NIPS 2021
Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning
AAAI 2021
SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients
NIPS 2021
Accelerated Stochastic Gradient-free and Projection-free Methods
ICML 2020
Momentum-Based Policy Gradient Methods
ICML 2020
Discrete Model Compression With Resource Constraint for Deep Neural Networks
CVPR 2020
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization
ICML 2019
Zeroth-Order Stochastic Alternating Direction Method of Multipliers for Nonconvex Nonsmooth Optimization
IJCAI 2019
Faster Gradient-Free Proximal Stochastic Methods for Nonconvex Nonsmooth Optimization
AAAI 2019