Feihu Huang

31 papers · 2019–2025 · 10 conferences · across top CS/AI conferences

Achievements

🏃 Academic Marathon (6) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🐝 Cross-Pollinator (11) 🗺️ Taxonomy Completionist (32) 🌍 Conference Polyglot (10) 🤝 Dynamic Duo (21) 🏆 Grand Slam 🔬 Deep Specialist (16) 💎 Century Club (31) 🚀 Conference Pioneer 🗃️ Keyword Collector (84) ⚡ Prolific Year (6) 🔥 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

Heng Huang (21) Shangqian Gao (10) Songcan Chen (6) Junyi Li (5) Yanfu Zhang (4) Xidong Wu (3) Wenhan Xian (3) Jian Pei (3) Zhengmian Hu (3) Xinrui Wang (2)

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