Second, they lack a specific focus on selecting a robust CA TE estimator.

ERM combines two well-understood techniques: mini-batch sampling and gradient-based optimization using backpropagation.

This paper studies the problem of entropy identity testing: given sample access to a distribution p and a fully described distribution q (both discrete distributions over a domain of size k), and the promise that either p = q or |H (p) H (q)| ε, where H () denotes the Shannon entropy, a tester needs to distinguish between the two cases with high probability.

This makes CDE particularly useful in critical application domains. However, interpretable CDE methods are understudied.

We show that any (possibly non-private) learning rule can be effectively transformed to a private learning rule with only a polynomial overhead in the mistake bound.

Such a phenomenon is illustrated in Figure 1 where the learned predictive function from the source data may significantly differ from the true function.

Let k (,) be the ν = 2 .5 Matérn kernel.