no code implementations • 22 Jan 2023 • Menachem Sadigurschi, Moshe Shechner, Uri Stemmer
Streaming algorithms are typically analyzed in the oblivious setting, where we assume that the input stream is fixed in advance.
no code implementations • 8 Dec 2022 • Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer
We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP).
no code implementations • 21 Jan 2022 • Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
The vast majority of the work on adaptive data analysis focuses on the case where the samples in the dataset are independent.
1 code implementation • NeurIPS 2021 • Menachem Sadigurschi, Uri Stemmer
We revisit the fundamental problem of learning Axis-Aligned-Rectangles over a finite grid $X^d\subseteq{\mathbb{R}}^d$ with differential privacy.
no code implementations • 3 Oct 2018 • Idan Attias, Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
For the $\ell_2$ loss, does every function class admit an approximate compression scheme of polynomial size in the fat-shattering dimension?
no code implementations • 21 May 2018 • Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi
We give an algorithmically efficient version of the learner-to-compression scheme conversion in Moran and Yehudayoff (2016).