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Private PAC Learning May be Harder than Online Learning
We continue the study of the computational complexity of differentially private PAC learning and how it is situated within the foundations of machine learning.
Oracle-Efficient Differentially Private Learning with Public Data
Due to statistical lower bounds on the learnability of many function classes under privacy constraints, there has been recent interest in leveraging public data to improve the performance of private learning algorithms.
Efficient Statistics for Sparse Graphical Models from Truncated Samples
We show that ${\mu}$ and ${\Sigma}$ can be estimated with error $\epsilon$ in the Frobenius norm, using $\tilde{O}\left(\frac{\textrm{nz}({\Sigma}^{-1})}{\epsilon^2}\right)$ samples from a truncated $\mathcal{N}({\mu},{\Sigma})$ and having access to a membership oracle for $S$.
Quantifying Error in the Presence of Confounders for Causal Inference
We show that many popular methods, including back-door methods can be considered as weighting or representation learning algorithms, and provide general error bounds for their causal estimates.
