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Learning Sparse Support under Differential Privacy: Adaptive Algorithms and Minimax Limits
We study exact support recovery under $(ε,δ)$-differential privacy in sparse high-dimensional linear regression. We introduce Saturated Propose-test-release, a general mechanism that privately releases the output of a discrete selector with probability one once its stability certificate reaches a fi
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