Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper proposes the use of an MLP that predicts both mean and std for predicting the time of a surgical operation.
They extend the method also to Laplace distribution.
The method is simple, not novel, but the combination of the method and the application is novel.
What worries me is the marginal improvements reported in table 1. Most of the improvement comes from the use of an MLP, more than the prediction of the variance - see the difference between Gaussian and Gaussian HS, and Laplace and Laplace HS.
My conclusion is that the choice of the distribution/loss in conjunction with the use of an MLP is more important than anything else, and in particular, it is more important than predicting variance (which is the main point of the abstract).

review_title: good motivation but incremental improvements

final_score: rating: 5: Marginally below acceptance threshold
review_content: Summary:

This work models the distribution of surgery durations using unimodal
parameteric distributions (viz., Gaussian and Laplace) by regressing their
parameters using multi-layer perceptrons based on patient and clinical
environment attributes.

Using the uncertainty (or standard-deviation) estimates, they report
improvements of 18% in scheduling surgeries.

This is the first application of heteroscedastic neural regression to clinical
medical data.


Comments:
1. In Table 1, it is not clear what "Current Method" corresponds to.


Assessment:

Clarity:
The method has been presented clearly, with all the details to reproduce the
results (although it is not clear if the medical data is publicly available).

Novelty & Significance:

The method presented uses multi-layer perceptrons (MLPs) for regressing
parameters of univariate unimodal parametric distributions, which is not quite
novel, and is a simple application MLP to this specific domain (clinical medical
data).
review_title: Review

Decision: Reject