Paper:
Interactive Structure Learning with Structural Query-by-Committee
In this work, we introduce interactive structure learning, a framework that unifies many different interactive learning tasks. We present a generalization of the queryby-committee active learning algorithm for this setting, and we study its consistency and rate of convergence, both theoretically and empirically, with and without noise.

Review:
This paper formulates a framework which unifies several interactive learning problems with a structure such as interactive clustering. Next, the authors show that QBC can be generalized and kernelized to solve the problems in the framework. The consistency and rate of convergence are analyzed.   I do not think I could judge the novelty of the theorems and proofs well. Thus, my comments focus on the practicality of the proposed algorithm, which I believe is relevant to its significance and the impact to the field.   Strength:  I think the direction of applying QBC to interactive learning settings other than active learning is promising. QBC might not be that practical in active learning problems because state-of-the-art models are usually neural network and modern neural networks have huge version space and there are lots of very different and equally good local minimums. It is also not easy to sample from neural networks efficiently (dropout only define the uncertainty on a small number of weights). However, the state-of-the-art clustering methods like GMM are usually very simple and easy to sample.  The writing is also very clear. Reading the paper is like reading a chapter of a textbook.  Weakness (or more like suggestions for future improvements):  Although paper tries to address the practical concerns (handling the noisy cases and only need to sample two parameters at a time), the method still has some practical issues. For example, QBC-based methods are vulnerable to noise. If we could quantify the level of noise at the beginning (e.g., knows the beta in eq. (1)), we can modify QBC to have nice guarantees. However, we usually do not know this information before collecting the data. In fact, most of the time, we do not even know which class of models with what kinds of hyper-parameters will perform very well before collecting enough data. Furthermore, clustering tasks are usually more ambiguous. There might also be many equally good local minimums in complicated clustering problems and models. Different annotators often have their own solutions from different local minimums. This means the noise level is usually high and hard to be predicted. Without knowing beta, I do not know how robust the structure QBC would be. Even if we know the noise level, it is possible that we spend lots of effort searching g* by eliminating lots of equally good local minimums, but we find that g* is not good enough eventually because the model assumption has some flaws or the original model becomes too simple as more samples are collected.  The experiments are only done using toy data and do not compare with other strategies such as uncertainty sampling. Is the algorithm scalable? Is it possible to know the time complexity of the proposed structural QBC when choosing the next example with respect to all the hyper-parameters? Can this study really motivate more people to use QBC in practice?   I understand that this is a theoretical paper, and my educated guess is that the theoretical part of the paper is very solid even though the practicality of the method has not been fully demonstrated. That's the main reason why I vote a clear accept.   Minor suggestions: 1. I can only find the explanation of notation \nu in Algorithm 1. Suggest mentioning the meaning of \nu in the text as well. 2. In Algorithm 2, it does not say how to determine n_t. What does the "appropriate number" mean in line 225? It is hard to find the answer in [30]. 3. Line 201, it should be Gilad-Bachrach et al. [16] investigated ... 4. In the related work section, I recommend the authors to cite some other efforts, especially the ones which try to make QBC based methods more practical and test its performances on multiple real-world datasets (e.g., Huang et al.)  Huang, Tzu-Kuo, et al. "Efficient and parsimonious agnostic active learning." Advances in Neural Information Processing Systems. 2015.  After rebuttal I think all reviewers agree that this is a solid theoretical work, so my judgment remains the same. The authors do not address the practicality issues in the rebuttal which prevents me to increase the overall score to an even higher level.