Projects per year
Personal profile
Research interests
Mathematics & Statistics - Optimization
Computer Science / Information Technology - Game theory
Industrial engineering - Operations research
Industrial engineering - Decision making
Computer Science / Information Technology - Game theory
Industrial engineering - Operations research
Industrial engineering - Decision making
Related documents
Education/Academic qualification
PhD in Machine Learning, PhD, Georgia Institute of Technology
2023
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Dive into the research topics where Zihao HU is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
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Projects
- 1 Active
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Adaptive Learning for Online Ad Display in Non-stationary Environments
1/10/25 → 30/09/28
Project: Research
Research output
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Faster margin maximization rates for generic and adversarially robust optimization methods
WANG, G., HU, Z., GENTILE, C., MUTHUKUMAR, V. & ABERNETHY, J., 9 Oct 2025, In: Mathematical Programming. 41 p.Research output: Contribution to journal › Journal Article › peer-review
Open Access -
Riemannian Online Optimistic Algorithms With Dynamic Regret
Wang, X., Yuan, D., Hong, Y., Hu, Z., Wang, L. & Shi, G., 2 Apr 2025, In: IEEE Transactions on Automatic Control. 70, 10, p. 6481-6496 16 p.Research output: Contribution to journal › Journal Article › peer-review
2 Link opens in a new tab Citations (Scopus) -
Extragradient Type Methods for Riemannian Variational Inequality Problems
Hu, Z., Wang, G., Wang, X., Wibisono, A., Abernethy, J. & Tao, M., 2024, In: Proceedings of Machine Learning Research. 238, p. 2080-2088 9 p.Research output: Contribution to journal › Conference article published in journal › peer-review
1 Link opens in a new tab Citation (Scopus) -
Faster Margin Maximization Rates for Generic Optimization Methods
Wang, G., Hu, Z., Muthukumar, V. & Abernethy, J., 2023, Advances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023. Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M. & Levine, S. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 36).Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review
3 Link opens in a new tab Citations (Scopus) -
Minimizing Dynamic Regret on Geodesic Metric Spaces
Hu, Z., Wang, G. & Abernethy, J., 2023, In: Proceedings of Machine Learning Research. 195, p. 4336-4383 48 p.Research output: Contribution to journal › Conference article published in journal › peer-review
3 Link opens in a new tab Citations (Scopus)