Explore kernel density estimation through density constrained near neighbor search in this IEEE conference talk. Delve into kernel functions, density motivation, and analysis of random sampling. Examine importance sampling estimators and locality sensitive hashing techniques. Investigate the Charikar-Siminelakis'17 approach, including simplifying assumptions and ideal importance sampling. Learn about using Andoni-Indyk LSH for recovery, collision probabilities, and density constraints. Understand query time, space complexity, and the basics of data-dependent LSH. Analyze log-density, density evolution of query buckets, and the effect of hashing on log-densities. Conclude with technical steps and open questions in this field of computational statistics and machine learning.