Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: The authors break the double blind anonymity with the code link provided. I'll leave how to deal with this to the meta reviewer. 

The authors provide a method to modify GRFs to be used for classification. The idea is simple and easy to get through, the writing is clean. The method boils down to using a latent variable that acts as a "pseudo-regressor" that is passed through a sigmoid for classification. The authors then discuss learning and inference in the proposed model, and propose two different variants that differ on scalability and a bit on performance as well. The idea of using the \xi transformation for the lower bound of the sigmoid was interesting to me -- since I have not seen it before, its possible its commonly used in the field and hopefully the other reviewers can talk more about the novelty here. The empirical results are very promising, which is the main reason I vote for weak acceptance. I think the paper has value, albeit I would say its a bit weak on novelty, and I am not 100% convinced about the this conference being the right fit for this paper. The authors augment MRFs for classification and evaluate and present the results well. 

Can the authors intuit why random forests and neural nets dont perform as well ? It seems there are many knobs one can tune to get better performance, so I will take the presented results with a grain of salt. Also, it seems one can also use other "link" functions with MRFs (similar to link functions in generalized linear models) to not just do logistic but other possible losses as well. How about multiclass classification using softmax ? I think such generalizations would make this paper lot stronger. 
review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: TITLE
Gaussian Conditional Random Fields for Classification

REVIEW SUMMARY
A well justified approach to structured classification with demonstrated good performance. 

PAPER SUMMARY
The paper presents methods for structured classification based on a Gaussian conditional random field combined with a softmax Bernoulli likelihood. Methods for inference and parameter learning are presented both for a "Bayesian" and maximum likelihood version. The method is demonstrated on several data sets.

QUALITY
In general, the technical quality of the paper is good. Except for minor typos, derivations appear to be correct, although I did not check everything in detail. 

CLARITY
The paper could be improved by a careful revision with focus on improving grammar, but as it stands the paper is easy to follow.
It is not clear to me exactly how the numbers in Table 1 were computed. Is this based on 10-fold crossvalidation as in the following tables?

ORIGINALITY
I am not familiar enough with the field to assess the novelty of the contribution. It would be great if the paper provided a better overview of competing structured classification methods.

FURTHER COMMENTS

"structured classification" ?

"It was shown" -> We show

"for given"

Is the second sum over k=1 to K in eq. 1 a mistake?

"We void" -> We avoid


review_title: Official Blind Review #3

final_score: rating: 1: Reject
review_content: The work involves modifiying gaussian conditional random fields to work for classification problems instead of regression problems. The main idea is to apply a bernoulli distribution on top of the regression values to convert them to work with binary classification problems. Two variations are discussed along with the inference and learning methodology. The inference can be done using numerical approximation and learning using variational methods and is still untracktable. Comparisons with other modeling strategies is done using experiments.

The paper is incremental and doesn't really provide improvements to learning parameters (or at least there is no theory showing this in the paper). The experiments do not seem satisfactory as discussed below.
a) Applying a bernoulli distribution on the output of the GCRF seems trivial. It is not very clear when the GCRFBCb model would be better than the GCRFBCnb. The learning procedure is untracktable and hard to follow on why this might provide better results.
b) The datasets (music classification and gene classification) don't seem to be good datasets for structured predictions i.e. the interaction needed between the nodes is not clear. Since they are multilabel problems, one could have just modeled the system with N independent nodes or design a multinomial distribution instead of only for binary classification.
c) There should be more thorough fine-tuning of other models, for e.g. in the ski lifts experiment, the CRF does much worse than logistic regression in the results. This is most likely because the parameters were not initialized properly using normal tricks like using logistic regression. Typically for truly structured problems, CRFs do better than their logistic regression counter parts. It is also not clear how the other models (CRF and SSVM) pairwise potentials were modeled.

It would really help to make this paper stronger by showing the new modeling technique does better than CRFs (that are tuned properly) on better structured datasets. It would be good to have a discussion on when this model would do worse than the other structured models and why.

review_title: Official Blind Review #1

Meta-review: Main content:

Blind review #2 summarizes it well:

The authors provide a method to modify GRFs to be used for classification. The idea is simple and easy to get through, the writing is clean. The method boils down to using a latent variable that acts as a "pseudo-regressor" that is passed through a sigmoid for classification. The authors then discuss learning and inference in the proposed model, and propose two different variants that differ on scalability and a bit on performance as well. The idea of using the \xi transformation for the lower bound of the sigmoid was interesting to me -- since I have not seen it before, its possible its commonly used in the field and hopefully the other reviewers can talk more about the novelty here. The empirical results are very promising, which is the main reason I vote for weak acceptance. I think the paper has value, albeit I would say its a bit weak on novelty, and I am not 100% convinced about the this conference being the right fit for this paper. The authors augment MRFs for classification and evaluate and present the results well. 

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Discussion:

As blind review #1 points out:

Even from the experiments (including the new traffic one), it is unclear how much better the method is either because we don't know if the improvements are statistically significant and that in many of the results, unstructured models like RF or logistic regression are very competitive casting some doubt on whether these datasets were well suited for structured prediction.

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This paper is a desk reject as review #2's points out that anonymity was broken by the inclusion of a code link that reveals the authorship, which is true as a simple search on the GitHub user "andrijaster" immediately brings us to https://arxiv.org/pdf/1902.00045.pdf which is a draft of this submission showing all author names.
Decision: Reject