Venue: ICLR 2018 conference

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: This paper discusses an application of survival analysis  in social networks.

While the application area seems to be pertinent, the statistics as presented in this paper are suboptimal at best. There is no useful statistical setup described (what is random? etc etc), the interplay between censoring and end-of-life is left rather fuzzy, and mentioned clustering approaches are extensively studied in the statistical literature in so-called frailty analysis. The setting is also covered in statistics in the extensive literature on repeated measurements  and even time-series analysis. It's up to the authors discuss similarities and differences of results of the present approach and those areas.

The numerical result is not assessing the different design decisions of the approach (why use a Kuyper loss?) in this empirical paper.


review_title: Brittle application of deep learning to statistical survival analysis

final_score: rating: 6: Marginally above acceptance threshold
review_content: Authors provide an interesting loss function approach for clustering using a deep neural network. They optimize Kuiper-based nonparametric loss and apply the approach on a large social network data-set.  However, the details of the deep learning approach are not well described. Some specific comments are given below.

1.Further details on use of 10-fold cross validation need to be discussed including over-fitting aspect.
2. Details on deep learning, number of hidden layers, number of hidden units, activation functions, weight adjustment details on each learning methods should be included.

3. Conclusion section is very brief and can be expanded by including a discussion on results comparison and  over fitting aspects in cross validation. Use of Kuiper-based nonparametric loss should also be justified as there are other loss functions can be used under these settings.

review_title: A Deep Learning Approach for Survival Clustering without End-of-life Signals

final_score: rating: 6: Marginally above acceptance threshold
review_content: Pros:
The paper is a nice read, clearly written, and its originality is well stated by the authors, “addressing the lifetime clustering problem without end-of-life signals for the first time”. I do not feel experienced enough in the field to evaluate the significance of this work.

The approach proposed in the manuscript is mainly based on a newly-designed nonparametric loss function using the Kuiper statistic and uses a feed-forward neural network to optimize the loss function. This approach does challenge some traditional assumptions, such as the presence of end-of-life signals or the artificial defined timeouts. Instead of giving a clear end-of-life signal, the authors specify a probability of end-of-life that permits us to take into account the associated uncertainty. By analyzing a large-scale social network dataset, it is shown that the proposed method performs better on average than the other two traditional models.

Cons:       
I think that the main drawback of the paper is that the structure of the neural network and the deep learning techniques used for optimizing the loss function are not explained in sufficient detail. 
review_title: This manuscript presents a novel approach to survival clustering and compares it with some competing models.  

Meta-review: The submission proposes a Kuiper statistic based loss function for survival clustering.  This loss function is applied to train a deep network.  Results are presented on a Friendster dataset.

This submission received borderline/mixed reviews.  The primary concerns were: justification of the Kuiper loss, lack of details of the experimental setup, writing style.  In the end, these concerns remain.

Of particular importance is the justification and experimental validation of the Kuiper statistic.  Although it seems a reasonable choice, from the authors' response to R3: "We now also report results for Kolmogorov-Smirnov loss. Although the difference in performance between the two loss functions is not significant in the Friendster dataset, Kuiper loss has higher statistical power in distinguishing distribution tails [Tygert 2010]."  If this theoretical result from [Tygert 2010] is relevant, it should be possible to demonstrate this experimentally.  If such differences are irrelevant for the data of interest, the paper should perhaps be reframed with a better discussion of available statistics and literature (cf. Reviewer 2), and a more general presentation de-emphasizing modeling choices that may have limited practical relevance.

Decision: Reject