| Interior-point methods strike back: Solving the wasserstein barycenter problem D Ge, H Wang, Z Xiong, Y Ye Advances in neural information processing systems 32, 2019 | 27 | 2019 |
| Learning from multiple annotator noisy labels via sample-wise label fusion Z Gao, FK Sun, M Yang, S Ren, Z Xiong, M Engeler, A Burazer, L Wildling, ... European Conference on Computer Vision, 407-422, 2022 | 11 | 2022 |
| From an interior point to a corner point: smart crossover D Ge, C Wang, Z Xiong, Y Ye arXiv preprint arXiv:2102.09420, 2021 | 4 | 2021 |
| Low-rank traffic matrix completion with marginal information Z Xiong, Y Wei, R Xu, Y Xu Journal of Computational and Applied Mathematics 410, 114219, 2022 | 3 | 2022 |
| Fairwasp: Fast and optimal fair wasserstein pre-processing Z Xiong, N Dalmasso, A Mishler, VK Potluru, T Balch, M Veloso Proceedings of the AAAI Conference on Artificial Intelligence 38 (14), 16120 …, 2024 | 1 | 2024 |
| Computational Guarantees for Restarted PDHG for LP based on "Limiting Error Ratios" and LP Sharpness Z Xiong, RM Freund arXiv preprint arXiv:2312.14774, 2023 | 1 | 2023 |
| On the Relation Between LP Sharpness and Limiting Error Ratio and Complexity Implications for Restarted PDHG Z Xiong, RM Freund arXiv preprint arXiv:2312.13773, 2023 | 1 | 2023 |
| Fair Wasserstein Coresets Z Xiong, N Dalmasso, VK Potluru, T Balch, M Veloso arXiv preprint arXiv:2311.05436, 2023 | | 2023 |
| Additional Results and Extensions for the paper “Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization” Z Xiong, RM Freund https://zikaixiong.github.io/papers/fwErmReport.pdf, 2023 | | 2023 |
| Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization Z Xiong, RM Freund arXiv preprint arXiv:2208.13933, 2022 | | 2022 |
Yinyu YeK.T. Li Professor of Engineering, Stanford UniversityVerified email at stanford.edu
