Eduardo Figueiredo

Clustered Randomized Smoothing for Stochastic Prediction Functions

Clustered $\alpha$-smoothing is proposed, a framework that partitions noisy samples using an arbitrary clustering algorithm, applies $\alpha$-smoothing locally within each cluster, and combines the resulting predictions into a mixture distribution, derived from interpreting the smoothing distribution as a mixture of $\alpha$-smoothers.