Reviews: Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning

Originality: The combination of sampling in areas of'greater interest' while adjusting to the underlying distribution appears in many active learning works, but the objective in (1) is novel and approaching both in a unified framework is challenging. The lower bounding of the optimization problem is also new Quality: The experimental results are very thorough and show the improvement of the proposed method over random sampling as well as several other baselines. And the exploration of effect of tuning parameters and initial sample size is excellent. However the theoretical contributions appear incomplete. The significant theoretical contribution is the (mislabelled) Theorem 2, and both the statement and proof of this is extremely informal.