Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 3: Clear rejection
review_content: I feel the proposed approach is just a special case of the well-known FJLT, where a sparse +-1 random matrix is used to multiply a signal efficiently while preserving inner products of signal vectors.

The only difference is that in the proposed approach the sampling is without replacement (i.e., one entry can only contribute to one bucket). I don't think it is an important difference. The theoretical results don't show why without replacement sampling matters either.
review_title: Lack of novelty

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes a technique to compress high-dimensional sparse vectors while preserving Hamming distance and inner products. The approach amounts to multiplying the sparse (say, column) vector by a sparse matrix with mutually orthogonal binary or +/-1-valued rows. 

The work reads well and is clearly presented. However, the work fails to mention directly related approaches such as the Sparse JL transform or the Fast  JL transform. From my understanding these approaches share most (all?) of the benefits of the proposed approach, so I have concerns about the novelty. At minimum a discussion about the differences/tradeoffs compared to these prior techniques is required.

I must say though that I am not very familiar with this area or the mentioned approaches, so it is difficult for me to fully evaluate novelty.
review_title: Clearly written but unsure of novelty

Decision: Reject