Author

Di Wang

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Preprint Oct 2026

Differentially Private Sparse Reward Estimation with Preference Feedback

Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive preference data necessitates rigorous differential privacy (DP) guarantees to prevent user information leakage. Existing private alignment methods typi...

Meng Ding, Ming-Xi Lei, Jie Zhang et al. · 1 citation

#machine learning Preprint Oct 2026

On the Limits of Sparse Linear Model with Shuffled Differential Privacy

By extending the score attack framework to user-level data, a new lower bound of $\Omega$ is established for any user-level $(\epsilon,\delta)$-DP, showing that the proposed algorithm is near-optimal with only a gap of the factor $\log(d/k)$.

Yan Zhang, Jiong Tuo, Meng Ding et al. · 0 citations