Smoothed Analysis of Online and Differentially Private Learning

17 Jun 2020Nika HaghtalabTim RoughgardenAbhishek Shetty

Practical and pervasive needs for robustness and privacy in algorithms have inspired the design of online adversarial and differentially private learning algorithms. The primary quantity that characterizes learnability in these settings is the Littlestone dimension of the class of hypotheses [Ben-David et al., 2009, Alon et al., 2019]... (read more)

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