To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method.

As a result, a naïve ERM learning process will be biased towards the majority classes, making it difficult to generalize to the minority classes.

Its derivation, based on Lu et al.[54] is as follows. For the HMC baseline, we use the default implementation of NUTS in Pyro. In Table 7, we present the detailed, non-averaged results to complement Table 4. In both cases, we observe that the performance of the refined posterior approaches HMC's. C.2 Textclassification We further validate the proposed method on text classification problems.

Thesecond stage then introduces unlabeled data withdisjoint classesin a semi-supervised scheme to diversify these priors and achieve generalization. We assess our method on both synthetic data and real collected point clouds.