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Found 4 papers, 1 papers with code
Differentially Private Sampling from Distributions
We demonstrate that, in some parameter regimes, private sampling requires asymptotically fewer observations than learning a description of $P$ nonprivately; in other regimes, however, private sampling proves to be as difficult as private learning.
What Can We Learn Privately?
Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples.
Sublinear Algorithms for Approximating String Compressibility
We raise the question of approximating the compressibility of a string with respect to a fixed compression scheme, in sublinear time.
Data Structures and Algorithms
