Miduo Cui — accepted AI-conference papers

2026

DRIVE: Distributional and Retrieval-Augmented Bidding with Value Evaluation

Miduo Cui, Haochen Wang, Shangqin Mao, Xun Yang, Qianlong Xie, Xingxing Wang +3

ICML 2026poster

Auto-bidding is a core component of real-time advertising systems, where decisions must optimize long-term performance under budget and cost constraints, while online exploration is prohibitively risky. Offline reinforcement learning and, more recently, Transformer-based sequence modeling have shown…

Cited by 1 Source ↗Scholar ↗

2026

Peak-Return Greedy Slicing: Subtrajectory Selection for Transformer-based Offline RL

Zhiwei Xu, Miduo Cui, Dapeng Li, Zhihao Liu, Haifeng Zhang, Hangyu Mao +2

ICLR 2026poster

Offline reinforcement learning enables policy learning solely from fixed datasets, without costly or risky environment interactions, making it highly valuable for real-world applications. While Transformer-based approaches have recently demonstrated strong sequence modeling capabilities, they typica…

Cited by 0 Source ↗Scholar ↗