| 2024 | ICLR | Fair and Efficient Contribution Valuation for Vertical Federated Learning. | Zhenan Fan, Huang Fang, Xinglu Wang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Yong Zhang |
| 2022 | ICDE | Improving Fairness for Data Valuation in Horizontal Federated Learning. | Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu, Yong Zhang |
| 2021 | ICLR | Fast convergence of stochastic subgradient method under interpolation. | Huang Fang, Zhenan Fan, Michael P. Friedlander |
| 2020 | AISTATS | Greed Meets Sparsity: Understanding and Improving Greedy Coordinate Descent for Sparse Optimization. | Huang Fang, Zhenan Fan, Yifan Sun, Michael P. Friedlander |
| 2020 | ICML | Online mirror descent and dual averaging: keeping pace in the dynamic case. | Huang Fang, Nick Harvey, Victor S. Portella, Michael P. Friedlander |
| 2019 | ACSSC | Bundle methods for dual atomic pursuit. | Zhenan Fan, Yifan Sun, Michael P. Friedlander |
| 2019 | SDM | Fast Training for Large-Scale One-versus-All Linear Classifiers using Tree-Structured Initialization. | Huang Fang, Minhao Cheng, Cho-Jui Hsieh, Michael P. Friedlander |
| 2015 | ICML | Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection. | Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael P. Friedlander, Hoyt A. Koepke |
| 2013 | ICML | Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. | Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger |
| 2012 | ICASSP | Robust inversion via semistochastic dimensionality reduction. | Aleksandr Y. Aravkin, Michael P. Friedlander, Tristan van Leeuwen |