← Authors

Hongchang Gao

24 papers · 2015–2025 · 6 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (10) 🐝 Cross-Pollinator (9) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🐺 Lone Wolf (3) πŸ”¬ Deep Specialist (12) πŸ† Keyword Champion (2) πŸ—ƒοΈ Keyword Collector (88) ⚑ Prolific Year (5) πŸ’Ž Century Club (24) πŸ”₯ Unstoppable (8) ❓ The Questioner

Conferences

IJCAI (7) ICML (6) AAAI (4) AISTATS (3) ICCV (2) NIPS (2)

Top co-authors

Heng Huang (9) Yihan Zhang (3) Xinwen Zhang (2) Bin Gu (2) Xiaoqian Wang (2) Xinghua Shi (2) Richard Souvenir (2) Chen Song (1) Bin Li (1) Wenkang Zhan (1)

Keywords

stochastic optimization (8) federated learning (4) stochastic gradient descent (4) compositional optimization (4) sample complexity (3) distributed learning (3) nonconvex optimization (3) distributed optimization (3) bilevel optimization (3) frank-wolfe method (3) communication complexity (2) auc maximization (2) communication efficiency (2) gradient descent (2) minimax optimization (2) decentralized optimization (2) stochastic gradient (2) constrained optimization (2) convergence rate (2) image generation (1)

Papers

Federated Stochastic Bilevel Optimization with Fully First-Order Gradients IJCAI 2025
A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization ICML 2024
Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning AISTATS 2024
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate AISTATS 2024
A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization ICML 2024
Discriminative Forests Improve Generative Diversity for Generative Adversarial Networks AAAI 2024
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models ICCV 2023
Federated Compositional Deep AUC Maximization NIPS 2023
Distributed Stochastic Nested Optimization for Emerging Machine Learning Models: Algorithm and Theory AAAI 2023
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network AISTATS 2023
Communication-Efficient Stochastic Gradient Descent Ascent with Momentum Algorithms IJCAI 2023
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum ICML 2022
Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems AAAI 2022
Gradient-Free Method for Heavily Constrained Nonconvex Optimization ICML 2022
Fast Training Method for Stochastic Compositional Optimization Problems NIPS 2021
On the Convergence of Communication-Efficient Local SGD for Federated Learning AAAI 2021
On the Convergence of Stochastic Compositional Gradient Descent Ascent Method IJCAI 2021
Sample Efficient Decentralized Stochastic Frank-Wolfe Methods for Continuous DR-Submodular Maximization IJCAI 2021
Can Stochastic Zeroth-Order Frank-Wolfe Method Converge Faster for Non-Convex Problems? ICML 2020
Demystifying Dropout ICML 2019
Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models IJCAI 2018
Deep Attributed Network Embedding IJCAI 2018
Joint Generative Moment-Matching Network for Learning Structural Latent Code IJCAI 2018
Multi-View Subspace Clustering ICCV 2015