From: NIPS.cc/2018/Workshop/IRASL
title: Acceptance Decision
decision: Accept

From: NIPS.cc/2018/Workshop/IRASL/Paper47/AnonReviewer3
title: Novel work, which can be improved with more experiments on various tasks
review: The authors proposed a straight-forward algorithm to determine the minimal subsets of features that suffice for the model-agnostic decisions. The overall idea is novel and could be widely applied on different models. The paper itself is well written as well. 

However, the gist of paper is not super correlated to this workshop and the algorithm needs to be conducted on either more tasks or relatively large-scale datasets to support the conclusion.
rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper47/AnonReviewer1
title: Interesting approach but broader experimentation needed
review: The paper presents a method of interpreting classifier models by constructing a minimal set of input features that are sufficient for the decision.

Pros:
- An interesting and novel approach
- Theoretical foundation is sound

What can be improved:
- It is not clear how efficient is the procedure, especially when using large modern networks and large inputs.
- Experiments provided are on only one dataset relevant to this workshop (Beer reviews). And it somewhat non-standard and not very well benchmarked.
- Judging by the size of the appendices, the analysis is really in depth. Perhaps, it would be a good idea to use wide variety of tasks to justify your method better

rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: NIPS.cc/2018/Workshop/IRASL/Paper47/AnonReviewer2
title: Local and global model interpretability via backward selection and clustering
review: The authors define a notion of interpreting black box models by defining a sufficient input subset.  They describe an algorithm to produce this SIS and describe the properties of the algorithm.  They then run the algorithm on a number of datasets across text and image modalities and compare their algorithm to other algorithms.  To my knowledge, the work is original.  The paper is thorough and extremely well written.

So much time taken for all the math notation, why not take a bit of time to improve the figures and captions?

Figure 3 is too small to be intelligible.  Generally the captions could be improved.

rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct