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cs.LG2026
Actually, All Learnable Distribution Classes Are Privately Learnable
Mohammad Afzali, Hassan Ashtiani, Alex Bie +4
We show that every class of distributions that is learnable up to arbitrarily small target error in total variation distance with finitely many samples is also learnable under ($\v…
stat.ML2026
Private Evaluation of High-Dimensional Functions
Mohammad Afzali, Hassan Ashtiani, Adam Smith
We study differentially private black-box evaluation of arbitrary high-dimensional functions. Given a dataset and query access to a function ---whose sensitivity may be unkn…
stat.ML2026
Efficient Agnostic Learning of Univariate Densities Under Local Differential Privacy
Mohammad Afzali, Hassan Ashtiani, Sivan Sabato
We propose a general framework for non-interactive locally differentially private (LDP) learning of structured univariate distributions that can be approximated by piece-wise polyn…