NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:1349
Title:Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning

The paper proposed a novel algorithm for active learning in the multi class setting. The authors present a theoretical guarantee regarding the sparseness of the model as well as empirical evaluation across 6 datasets and comparing with 5 baseline methods. All reviewers tend for vote for acceptance, but do point out several areas of improvement and the authors provide feedback for. I strongly expect the final version of the paper to include the changes that address: - Formal statement and proof outline for Theorem 2. - Include the comparison of RVM, SVM and KMC method in the passive learning setting (mentioned in the author feedback), in order to help distinguish the benefit of the novel model alone, in addition to the combination of model and active sampling. - BvSB could also be used with the KMC/RVM method. Including those results would significantly increase the value of the study.