Then, we propose a series of DP-SignRP algorithms that leverage the robustness of the "sign flipping probability" of random projections.

A popular class of analysis methods, often called structural analysis, aims to extract this information using probing classifiers that predict linguistic properties from representations of trained models (e.g., Adi et al., 2017; Conneau et al., 2018; Hupkes et al., 2018; Tenney et al., 2019).

We study the infinite-horizon restless bandit problem with the average reward criterion, in both discrete-time and continuous-time settings.

Weproveupper and lower bounds on the amount ofqueries that are needed for an auditor to succeed within this framework.