| Improving fairness for data valuation in horizontal federated learning Z Fan, H Fang, Z Zhou, J Pei, MP Friedlander, C Liu, Y Zhang 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2440-2453, 2022 | 65 | 2022 |
| Fair and efficient contribution valuation for vertical federated learning Z Fan, H Fang, Z Zhou, J Pei, MP Friedlander, Y Zhang arXiv preprint arXiv:2201.02658, 2022 | 38 | 2022 |
| Online mirror descent and dual averaging: keeping pace in the dynamic case H Fang, NJA Harvey, VS Portella, MP Friedlander Journal of Machine Learning Research 23 (121), 1-38, 2022 | 33 | 2022 |
| Greed meets sparsity: Understanding and improving greedy coordinate descent for sparse optimization H Fang, Z Fan, Y Sun, M Friedlander International Conference on Artificial Intelligence and Statistics, 434-444, 2020 | 18 | 2020 |
| Improved Bounded Matrix Completion for Large-Scale Recommender Systems. H Fang, Z Zhang, Y Shao, CJ Hsieh IJCAI, 1654-1660, 2017 | 17 | 2017 |
| Improved convergence of differential private sgd with gradient clipping H Fang, X Li, C Fan, P Li The Eleventh International Conference on Learning Representations, 2023 | 13 | 2023 |
| Differentiable neuro-symbolic reasoning on large-scale knowledge graphs C Shengyuan, Y Cai, H Fang, X Huang, M Sun Advances in Neural Information Processing Systems 36, 2024 | 12 | 2024 |
| Fast training for large-scale one-versus-all linear classifiers using tree-structured initialization H Fang, M Cheng, CJ Hsieh, M Friedlander Proceedings of the 2019 SIAM International Conference on Data Mining, 280-288, 2019 | 10 | 2019 |
| Fast convergence of stochastic subgradient method under interpolation H Fang, Z Fan, M Friedlander International Conference on Learning Representations, 2020 | 6 | 2020 |
| MLN4KB: an efficient markov logic network engine for large-scale knowledge bases and structured logic rules H Fang, Y Liu, Y Cai, M Sun Proceedings of the ACM Web Conference 2023, 2423-2432, 2023 | 4 | 2023 |
| A dual approach for federated learning Z Fan, H Fang, MP Friedlander arXiv preprint arXiv:2201.11183, 2022 | 4 | 2022 |
| A hyperplane-based algorithm for semi-supervised dimension reduction H Fang, M Cheng, CJ Hsieh 2017 IEEE International Conference on Data Mining (ICDM), 101-110, 2017 | 4 | 2017 |
| Efficient greedy coordinate descent via variable partitioning H Fang, G Fang, T Yu, P Li Uncertainty in Artificial Intelligence, 547-557, 2021 | 3 | 2021 |
| Differentiable Neuro-Symbolic Reasoning on Large-Scale Knowledge Graphs. S Chen, Y Cai, H Fang, X Huang, M Sun NeurIPS, 2023 | 2 | 2023 |
| MQuinE: a cure for" Z-paradox''in knowledge graph embedding models Y Liu, H Fang, Y Cai, M Sun arXiv preprint arXiv:2402.03583, 2024 | 1 | 2024 |
| Safe-screening rules for atomic-norm regularization Z Fan, H Fang, MP Friedlander arXiv preprint arXiv:2107.11373, 2021 | 1 | 2021 |
| Cardinality-constrained structured data-fitting problems Z Fan, H Fang, MP Friedlander arXiv preprint arXiv:2107.11373, 2021 | | 2021 |
| First-order methods for structured optimization H Fang University of British Columbia, 2021 | | 2021 |
| 1 PROOFS H Fang, G Fang, T Yu, P Li | | |
Michael P. FriedlanderProfessor of Computer Science and Mathematics, University of British Columbia在 friedlander.io 的电子邮件经过验证
