A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
C-MinHash reduces the number of permutations needed for MinHash from thousands to just two.
MinHash and SimHash are the two widely adopted Locality Sensitive Hashing (LSH) algorithms for large-scale data processing applications. Deciding which LSH to use for a particular problem at hand is an important question, which has no clear answer in the existing literature. In this study, we provide a theoretical answ…
Minwise hashing (Minhash) is a widely popular indexing scheme in practice. Minhash is designed for estimating set resemblance and is known to be suboptimal in many applications where the desired measure is set overlap (i.e., inner product between binary vectors) or set containment. Minhash has inherent bias towards sma…
C-OPH improves One Permutation Hashing by using a shorter circulant permutation.
Two private algorithms estimate Jaccard similarity efficiently.
A new DP algorithm improves privacy in hashing and sampling for search and learning.
The study classifies Android malware using minhashing and Structural Equation Models.
Locality-sensitive hashing speeds up web app security testing.
New split-merge MCMC proposals improve efficiency and speed.
For a broad range of research, governmental and commercial applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As…