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2020 – today
- 2024
[j2]Zhenan Fan, Huang Fang, Michael P. Friedlander:
Cardinality-constrained structured data-fitting problems. Open J. Math. Optim. 5: 1-21 (2024)- [c14]Zhenan Fan, Huang Fang, Xinglu Wang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang:
Fair and Efficient Contribution Valuation for Vertical Federated Learning. ICLR 2024 - [c13]Yang Liu, Chuan Zhou, Huang Fang, Peng Zhang, Yiling Pang, Qingzhao Zhang, Zhao Li, Hongyang Chen:
Secure Causal Reasoning on Coarsened Graph for Privacy-aware Social Web Services. ICWS 2024: 557-567 - [i5]Yang Liu, Huang Fang, Yunfeng Cai, Mingming Sun:
MQuinE: a cure for "Z-paradox" in knowledge graph embedding models. CoRR abs/2402.03583 (2024) - 2023
- [c12]Huang Fang, Xiaoyun Li, Chenglin Fan, Ping Li:
Improved Convergence of Differential Private SGD with Gradient Clipping. ICLR 2023 - [c11]Shengyuan Chen, Yunfeng Cai, Huang Fang, Xiao Huang, Mingming Sun:
Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs. NeurIPS 2023 - [c10]Huang Fang
, Yang Liu, Yunfeng Cai, Mingming Sun:
MLN4KB: an efficient Markov logic network engine for large-scale knowledge bases and structured logic rules. WWW 2023: 2423-2432 - 2022
- [j1]Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander:
Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case. J. Mach. Learn. Res. 23: 121:1-121:38 (2022) - [c9]Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang:
Improving Fairness for Data Valuation in Horizontal Federated Learning. ICDE 2022: 2440-2453 - [i4]Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang:
Fair and efficient contribution valuation for vertical federated learning. CoRR abs/2201.02658 (2022) - [i3]Zhenan Fan, Huang Fang, Michael P. Friedlander:
A dual approach for federated learning. CoRR abs/2201.11183 (2022) - 2021
- [c8]Huang Fang, Zhenan Fan, Michael P. Friedlander:
Fast convergence of stochastic subgradient method under interpolation. ICLR 2021 - [c7]Huang Fang, Guanhua Fang, Tan Yu, Ping Li:
Efficient greedy coordinate descent via variable partitioning. UAI 2021: 547-557 - [i2]Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang:
Improving Fairness for Data Valuation in Federated Learning. CoRR abs/2109.09046 (2021) - 2020
- [c6]Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander:
Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. AISTATS 2020: 434-444 - [c5]Huang Fang, Nick Harvey, Victor S. Portella, Michael P. Friedlander:
Online mirror descent and dual averaging: keeping pace in the dynamic case. ICML 2020: 3008-3017 - [i1]Huang Fang, Nicholas J. A. Harvey, Victor S. Portella, Michael P. Friedlander:
Online mirror descent and dual averaging: keeping pace in the dynamic case. CoRR abs/2006.02585 (2020)
2010 – 2019
- 2019
- [c4]Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander:
Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization. SDM 2019: 280-288 - 2017
- [c3]Huang Fang, Minhao Cheng, Cho-Jui Hsieh:
A Hyperplane-Based Algorithm for Semi-Supervised Dimension Reduction. ICDM 2017: 101-110 - [c2]Huang Fang, Zhen Zhang, Yiqun Shao, Cho-Jui Hsieh:
Improved Bounded Matrix Completion for Large-Scale Recommender Systems. IJCAI 2017: 1654-1660
2000 – 2009
- 2009
- [c1]Xicheng Tan, Huang Fang:
3D Spatial Analysis Oriented Hybrid P2P Computing. CSO (2) 2009: 153-155
Coauthor Index
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