Summary by David Stutz 1 month ago
Zahavy et al. introduce the concept of ensemble robustness and show that it can be used as indicator for generalization performance. In particular, the main idea is to lift he concept of robustness against adversarial examples to ensemble of networks – as trained, e.g. through Dropout or Bayes-by-Backprop. Letting $Z$ denote the sample set, a learning algorithm is $(K, \epsilon)$ robust if $Z$ can be divided into $K$ disjoint sets $C_1,\ldots,C_K$ such that for every training set $s_1,\ldots,s_n \in Z$ it holds:
$\forall i, \forall z \in Z, \forall k = 1,\ldots, K$: if $s,z \in C_k$, then $l(f,s_i) – l(f,z)| \leq \epsilon(s_1,\ldots,s_n)$
where $f$ is the model produced by the learning algorithm, $l$ measures the loss and $\epsilon:Z^n \mapsto \mathbb{R}$. For ensembles (explicit or implicit) this definition is extended by considering the maximum generalization loss under the expectation of a randomized learning algorithm:
$\forall i, \forall k = 1,\ldots,K$: if $s \in C_k$, then $\mathbb{E}_f \max_{z \in C_k} |l(f,s_i) – l(f,z)| \leq \epsilon(s_1,\ldots,s_n)$
Here, the randomized learning algorithm computes a distribution over models given a training set.
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