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.

Recently, there has been a marked increase in interest regarding the generalization capabilities of unregularized gradient-based learning methods.

We name this problem Safe-RL-SW . Our step-wise violation constraint differs from prior expected violation constraint (Wachi & Sui, 2020; Efroni et al., 2020b; Kalagarla et al., 2021) in two aspects: (i) Minimizing the step-wise violation enables the agent to learn an optimal policy that avoids unsafe regions deterministically,