NeurIPS 2020

Provably Robust Metric Learning

Meta Review

This paper proposes a metric learning method for robust KNN inference against adversarial examples. Advantages / Main pain points: - First certifiable robust metric learning but lacks comparison to robust metric learning - Authors added in the rebuttal comparison of radius KNN to deep networks and showed good results on mnist and fashion mnist Inconvenient: - Lack of comparison to previous methods doing robust metric learning This paper received mixed initial scores, that sparked a fruitful discussion phase. A consensus emerged between reviewers and AC that the contributions outweigh the execution flaws, and therefore we recommend this work for acceptance. We encourage the authors to add all relevant previous works pointed by reviewers and to add also baselines of robust metric learning.