On the Limits of Sparse Linear Model with Shuffled Differential Privacy
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2610. 31612v1 Announce Type: new Abstract: We study learning a discrete distribution under person-level differential privacy in the batched setting.
We study learning a discrete distribution under person-level differential privacy in the batched setting. Given $n$ users, each contributing $m$ i.
arXiv:2609. 22783v1 Announce Type: new Abstract: We study differentially private covariance estimation in operator norm for mean-zero sub-Gaussian distributions with unknown covariance support and at most $k$ nonzero entries per row.
The paper introduces a differentially private framework for sparse reward estimation in reinforcement learning with human feedback (RLHF). It assumes that only a small subset of features influences human preferences and provides theoretical error bounds for both Sample-DP and Label-DP settings under the Bradley-Terry-Luce model. Empirical results show that the proposed methods outperform dense baselines on sentiment generation and safety alignment tasks, achieving a better privacy‑utility trade‑off.
arXiv:2605. 06259v3 Announce Type: replace Abstract: Within the $f$-DP framework, we derive a tight analysis of the trade-off function for Differentially Private Stochastic Gradient Descent (DP-SGD) with subsampling based on random allocation in which each sample is independently assigned to exactly one of $M$ minibatches per epoch, each minibatch corresponding to one of the $M$ SGD rounds within a single epoch.
arXiv:2607. 29675v1 Announce Type: cross Abstract: Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored.