The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences
We study distributionally robust PAC learning for the $0$--$1$-loss, where adversarial perturbations of the data distribution are constrained by a Cressie--Read divergence of order $k>1$ and radius $\rho\geq 0$. For hypothesis classes with VC dimension $d$, we establish realizable and agnostic sample-complexity bounds tight up to constant and logarithmic factors, respectively; ordinary empirical risk minimization attains both rates up to logarithmic factors. For target accuracy $\varepsilon\in(0,1)$ and confidence $\delta\in(0,1)$, their respective orders are \[ \max\!\left\{\frac{1}{\varepsilon}, \frac{\rho^{\frac 1{k-1}}}{\varepsilon^{k_\star}} \right\}\cdot(d+\log \delta^{-1}) \qquad\text{and}\qquad \max\!\left\{\frac{1}{\varepsilon^2}, \frac{\rho^{\frac1{k-1}}}{\varepsilon^{k_\star\vee 2}} \right\}\cdot(d+\log \delta^{-1}), \] where $k_\star={k}/{(k-1)}$. For every fixed $\rho>0$, robustness changes the realizable $\varepsilon$-dependence from $\varepsilon^{-1}$ to $\varepsilon^{-k_\star}$ as $\varepsilon\downarrow0$. In the agnostic case, for $1 1$, close its upper--lower gaps, and recover standard PAC learning rates as $\rho\to0$, unlike previous bounds that fail to interpolate correctly in this limit.