Multidimensional simplex transformations on [0,1]-bounded variables extend the free lunch for private dataset size estimation, refining sufficient statistics for differentially private simple linear regression via OLS with claimed analytical and numerical gains.
Revisiting differentially private linear regression: optimal and adaptive prediction & esti- mation in unbounded domain
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Refined Differentially Private Linear Regression via Extension of a Free Lunch Result
Multidimensional simplex transformations on [0,1]-bounded variables extend the free lunch for private dataset size estimation, refining sufficient statistics for differentially private simple linear regression via OLS with claimed analytical and numerical gains.