Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: 
###### Overall Recommendation
I vote for the “Weak Accept” decision for this paper. 

### Summary
This paper introduces a novel model termed Random Distance Prediction model, it can predict data distances in randomly projected space. The distance in projected space is used as the supervisory signal to learn representations without any manually labeled data, avoiding the concentration and inefficiency problem when dealing with high-dimensional tabular data. Their main contribution is extending the random distance in projected spaces to approximate the original distance information of the hight-dimensional tabular data effectively. Overall, the idea in this paper is interesting and effective, the experiment results in two typical unsupervised tasks (anomaly detection and clustering) also look very promising. However, the writing sometimes has unclear descriptions, given these clarifications in an author's response, I would be willing to increase the score.

### Strengths
1. The illustration of the authors' idea is clear and concise.
2. The theoretical analysis of the proposed method is solid and systematic, the validity of the subparts have been proven previously.
3. The experiment part is well organized. The RDP model are compared with several state-of-the-art unsupervised learning methods in 19 real-world datasets of various domains. The experimental setup is solid with realistic considerations, the results are very convincing and promising.
4. This paper provides sufficient detail for reproducing the results.

### Weaknesses
Lack of systematic description of the authors’ major contribution. The proposed model looks more like a combination of previous conclusions, which makes readers feel the core parts of this paper build heavily on previous work.

### Questions
1. What is the relation between “random distance prediction loss” and “task-dependent auxiliary loss”?
2. Are there any solutions and references about how to choose the task-dependent loss L_aux? 
3. Why you shade the second part of the loss function in Figure 1; 
4. How long is it for training the proposed model and getting the experiment results? Does the RDP model still outperform the other algorithms?

### Suggestions to improve the paper
1.  It would be better to reorganize Section 1 and Section 2, please describe the contribution in a more systematic way.
2. Add details for the architecture of the model, please give more descriptions about Figure 1.
3. It might also help to add an algorithm comparison box for the test time for the proposed method.

### Minor Edit Suggestions
1. It would be better to give more descriptions about Figure 1; the lower right part in Figure 1 is not explained in the caption; the shadow part in Figure 1 is not precise.
2. Figure 1 was bad organized, please make the legend readable size. 
3. I don't think there exist the proofs of Eqns. (2)-(4) in the reference paper (Vempala, 1998), which was written in Page 4. The number should be revised.
review_title: Official Blind Review #4

final_score: rating: 3: Weak Reject
review_content: This paper proposed a method of unsupervised representation by transforming a set of data points into another space while maintaining the pairwise distance as good as possible. The paper is well structured with background literatures, formula, as well as experiments to show the advantage of the proposed method. I find it generally interesting, with the following major concerns.

1. Representation or dimension reduction? If the original space is a structured space like Euclidean space, then effectively this paper's method coincides with regular distance preserving method in dimension reduction, and Johnson-Lindenstrauss theories. If the original space is not structured or doesn't naturally have a good distance measure, then the proposed method cannot work. For example, if the original dataset is a set of documents, and the task is to do representation learning to convert each document into a compact vector. However, there's no good distance metric for the document space. If TF-IDF is used, then the representation space also inherits TF-IDF type features which is not desired. If more advanced similarity is used for the document space, then the role of representation learning is not essential anymore as that similarity measure can already help the downstream tasks.

2. Section 3 the theoretical analysis. This part seems like a collection of previous works and contains minimal information about the proposed method.

3. Some writing issues, like page 4 line 7 about the equation numbering.
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: The authors discuss the a novel technique, called Random Distance Prediction, to learn rich features from domains where massive data are hard to produce; in particular, they focus on the two tasks of anomaly detection and clustering.
The paper is well written and understandable by a non-specialistic audience; introduction and references are adequate, and the theoretical analysis is reasonably explained, although the two optional losses should have been discussed more deeply.
Results are fairly supporting the authors' claim: however, the improvement in performances w.r.t. alternative approaches are limited in most cases, and the contribute of the optional losses is somehow unclear and inconsistent across different datasets. It would be interesting also to check how stable is the method (i.e. the losses) for different underlying network architectures, and also how RDP compares to very basic approaches employing dimensionality reduction algorithms such as t-SNE or UMAP.
review_title: Official Blind Review #2

Meta-review: The reviewers agree that this is an interesting paper but it required major modifications. After rebuttal, thee paper is much improved but unfortunately not above the bar yet. We encourage the authors to iterate on this work again.
Decision: Reject