distribution-learning

该论文证明了所有在总变差距离下可学习的分布类也都是可私有学习的，从而证实了Ashtiani的一个猜想，即在(ε,δ)-差分隐私约束下，只要目标误差可以任意小，有限样本下的可学习性依然成立。

This paper introduces sparse priors to improve theoretical guarantees on distribution learning, showing that under a sparse prior, the sample complexity scales as Ω(√(k/n)) instead of O(n^{-1/Θ(d)}), thereby mitigating the curse of dimensionality.