Reviews: Space and Time Efficient Kernel Density Estimation in High Dimensions

Overall the paper is an average paper but clearly written. This paper proposes an improvement of Charikar's approach to achieve sublinear kernel density estimation with linear space and linear time preprocessing. Experimental results focus mainly on Laplacian (L1 variant in the main submission and L2 variant added in supplement). The key observation for achieving linear space is to modify the previous HBE approach so that each hash table stores each point in the dataset with constant probability - in this way, the superlinear storage cost is overcome. However, my main complaint is in the experimental results.