Neural Regression Tree
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree. We empirically show the validity of our model by testing it on two challenging regression tasks where we establish the state of the art.
 This paper presents a new approach to regression via classification problem utilizing a hybrid model between a neural network and a decision tree. The paper is very well written and easy to follow. It presents results on two very similar regression tasks and claims state of the art performance on both. The paper however does not motivate its contributions sufficiently, and does not provide enough experimental results to justify their method. The authors could significantly improve the paper by spending more time motivating their work. For example, it is unclear why RvC is the best strategy for the tasks they study and what other tasks one should approach from a RvC standpoint. The paper would also be significantly more compelling if the strategy was applied to more varied tasks. Furthermore the two baseline models used are 11 and 34 years old respectively and i do not believe they represent a thorough review of the potential approaches to this problem. Significant work could also be done to explore the effect of using different neural network structures for the NRT - in this paper only a fairly simple 3 layer architecture is used. Section 4.4 is interesting and i believe the paper would be improved if more time was spent exploring the explanability of this new proposed model. Finally the scan method mentioned in the conclusion could have more emphasis placed on it in the text. Over all the paper is well written and easy to follow but is limited by its lack of well detailed motivation and insufficient baselines and applied tasks.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Regression Tree
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree. We empirically show the validity of our model by testing it on two challenging regression tasks where we establish the state of the art.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper presents a new approach to regression via classification problem utilizing a hybrid model between a neural network and a decision tree. The paper is very well written and easy to follow. It presents results on two very similar regression tasks and claims state of the art performance on both. The paper however does not motivate its contributions sufficiently, and does not provide enough experimental results to justify their method. The authors could significantly improve the paper by spending more time motivating their work. For example, it is unclear why RvC is the best strategy for the tasks they study and what other tasks one should approach from a RvC standpoint. The paper would also be significantly more compelling if the strategy was applied to more varied tasks. Furthermore the two baseline models used are 11 and 34 years old respectively and i do not believe they represent a thorough review of the potential approaches to this problem. Significant work could also be done to explore the effect of using different neural network structures for the NRT - in this paper only a fairly simple 3 layer architecture is used. Section 4.4 is interesting and i believe the paper would be improved if more time was spent exploring the explanability of this new proposed model. Finally the scan method mentioned in the conclusion could have more emphasis placed on it in the text. Over all the paper is well written and easy to follow but is limited by its lack of well detailed motivation and insufficient baselines and applied tasks.
assistant
 This paper presents a new approach to regression via classification problem utilizing a hybrid model between a neural network and a decision tree. The paper is very well written and easy to follow. It presents results on two very similar regression tasks and claims state of the art performance on both. The paper however does not motivate its contributions sufficiently, and does not provide enough experimental results to justify their method. The authors could significantly improve the paper by spending more time motivating their work. For example, it is unclear why RvC is the best strategy for the tasks they study and what other tasks one should approach from a RvC standpoint. The paper would also be significantly more compelling if the strategy was applied to more varied tasks. Furthermore the two baseline models used are 11 and 34 years old respectively and i do not believe they represent a thorough review of the potential approaches to this problem. Significant work could also be done to explore the effect of using different neural network structures for the NRT - in this paper only a fairly simple 3 layer architecture is used. Section 4.4 is interesting and i believe the paper would be improved if more time was spent exploring the explanability of this new proposed model. Finally the scan method mentioned in the conclusion could have more emphasis placed on it in the text. Over all the paper is well written and easy to follow but is limited by its lack of well detailed motivation and insufficient baselines and applied tasks.