Adversarial Laws of Large Numbers and Optimal Regret in Online Classification

no code implementations • 22 Jan 2021 • Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev

Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample.

General Classification Open-Ended Question Answering +1

Privately Learning Thresholds: Closing the Exponential Gap

no code implementations • 22 Nov 2019 • Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer

This problem has received much attention recently; unlike the non-private case, where the sample complexity is independent of the domain size and just depends on the desired accuracy and confidence, for private learning the sample complexity must depend on the domain size $X$ (even for approximate differential privacy).