CV

Education

• B.Sc. in Mathematics and Applied Mathematics, Tsinghua University, 2022
• M.Sc. in Computer Science, University of California, Los Angeles, 2024
• Ph.D in Computer Science, University of California, Los Angeles, 2027 (expected), advised by Prof. Quanquan Gu

Research Experience

• 2022-2024: Graduate Student Researcher.
  • UCLA Artificial General Intelligence Lab, Los Angeles, CA, USA
• Summer 2024: Research Intern
  • Microsoft Research Asia – Vancouver lab, Redmond, WA, USA

Service

• Conference Reviewers: AISTATS[2024-2025], ICLR[2024-2025], NEURIPS2024

• Journal Reviewers: JAIR

Publications

^* means Equal Contribution.

1. Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu. Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. ICML2023.
2. Qiwei Di*, Tao Jin*, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu. Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits. ICLR2024
3. Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu. Pessimistic nonlinear least-squares value iteration for offline reinforcement learning. ICLR2024
4. Yue Wu, Tao Jin*, Qiwei Di*, Hao Lou, Farzad Farnoud, Quanquan Gu. Borda Regret Minimization for Generalized Linear Dueling Bandits. ICML2024
5. Qiwei Di, Jiafan He, Quanquan Gu. Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. Preprint.
6. Binshuai Wang, Qiwei Di, Ming Yin, Mengdi Wang, Quanquan Gu, Peng Wei. Relative-Translation Invariant Wasserstein Distance. Preprint.
7. Runjia Li, Qiwei Di, Quanquan Gu. Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers . Preprint.