Bayesian inference is desirable but extremely challenging.

Neural Information Processing Systems http://nips.cc/

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a new pairwise clustering framework where nonparametric pairwise similarity is derived by minimizing the generalization error unsupervised nonparametric classifier. The proposed framework bridges the gap between clustering and multi-class classification, and explains the widely used kernel similarity for clustering. The authors also prove that the generalization error bound for the unsupervised plug-in classifier is asymptotically equal to the weighted volume of cluster boundary for low density separation. Based on the derived nonparametric pairwise similarity using the plug-in classifier, the authors propose a new nonparametric exemplar-based clustering method with enhanced discriminative capability compared to the exiting exemplar-based clustering methods.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The supplementary material is organized as follows. We first discuss additional related work and provide experiment details in Section 2 and Appendix B respectively. We evaluate the extent to which ensemble methods and adversarial training mitigate Simplicity Bias (SB) in Appendix E. Finally, we provide the proof of Theorem 1 ( k 1) Also recall that each dataset comprises at most one "simple" feature Our results in Section 4 hold on all three MNIST-CIFAR datasets. In this section, we supplement our results in Section 4 of the paper by showing that extreme simplicity bias (SB) persists across several model architectures and on synthetic as well as image-based datasets. Now, we study the effect of activation function and optimizer on extreme SB.