P
Ping Li
Researcher at Rutgers University
Publications - 141
Citations - 5385
Ping Li is an academic researcher from Rutgers University. The author has contributed to research in topics: Hash function & Estimator. The author has an hindex of 35, co-authored 139 publications receiving 4798 citations. Previous affiliations of Ping Li include Cornell University & Microsoft.
Papers
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Proceedings ArticleDOI
Very sparse random projections
TL;DR: This paper proposes sparse random projections, an approximate algorithm for estimating distances between pairs of points in a high-dimensional vector space that multiplies A by a random matrix R in RD x k, reducing the D dimensions down to just k for speeding up the computation.
Proceedings Article
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting
TL;DR: This work considers the DCG criterion (discounted cumulative gain), a standard quality measure in information retrieval, and proposes using the Expected Relevance to convert class probabilities into ranking scores.
Posted Content
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
Anshumali Shrivastava,Ping Li +1 more
TL;DR: This work presents the first provably sublinear time algorithm for approximateMaximum Inner Product Search (MIPS), and is also the first hashing algorithm for searching with (un-normalized) inner product as the underlying similarity measure.
Proceedings Article
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
Anshumali Shrivastava,Ping Li +1 more
TL;DR: In this paper, the authors presented the first provably sublinear time hashing algorithm for approximate maximum inner product search (MIPS), which is based on a key observation that the problem of finding maximum inner products, after independent asymmetric transformations, can be converted into a problem of approximate near neighbor search in classical settings.
Proceedings ArticleDOI
b-Bit minwise hashing
Ping Li,Christian Konig +1 more
TL;DR: In this paper, the theoretical framework of b-bit minwise hashing is established and an unbiased estimator of the resemblance for any b is provided, even in the least favorable scenario, using b=1 may reduce the storage space at least by a factor of 21.3 (or 10.7) compared to b=64 (or b=32), if one is interested in resemblance > 0.5.