3 papers
cs.LG2026
Differentially Private Model Merging
Qichuan Yin, Manzil Zaheer, Tian Li
In machine learning, privacy requirements at inference or deployment time often evolve due to changing policies, regulations, or user preferences. In this work, we aim to construct…
stat.ML2026
Overcoming the Incentive Collapse Paradox
Qichuan Yin, Ziwei Su, Shuangning Li
AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved. Recent work by Bastani and Cachon (2025); Sambasivan et al.…
stat.ML2024
Distribution-Free Fair Federated Learning with Small Samples
Qichuan Yin, Zexian Wang, Junzhou Huang +2
As federated learning gains increasing importance in real-world applications due to its capacity for decentralized data training, addressing fairness concerns across demographic gr…