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.
Part 3: Independence
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.