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Ajit P. Singh
Researcher at Microsoft
Publications - 23
Citations - 4520
Ajit P. Singh is an academic researcher from Microsoft. The author has contributed to research in topics: Statistical relational learning & Matrix decomposition. The author has an hindex of 16, co-authored 23 publications receiving 4152 citations. Previous affiliations of Ajit P. Singh include University of Washington & Carnegie Mellon University.
Papers
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Journal Article
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
TL;DR: It is proved that the problem of finding the configuration that maximizes mutual information is NP-complete, and a polynomial-time approximation is described that is within (1-1/e) of the optimum by exploiting the submodularity of mutual information.
Proceedings ArticleDOI
Relational learning via collective matrix factorization
Ajit P. Singh,Geoffrey J. Gordon +1 more
TL;DR: This model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems, which can handle any pairwise relational schema and a wide variety of error models.
Proceedings ArticleDOI
Near-optimal sensor placements in Gaussian processes
TL;DR: A mutual information criteria is proposed, and it is proved that finding the configuration that maximizes mutual information is NP-complete, and a polynomial-time approximation is described that is within (1 -- 1/e) of the optimum by exploiting the submodularity of the criterion.
Book ChapterDOI
Patterns of influence in a recommendation network
TL;DR: In this paper, the authors investigate a large person-to-person recommendation network, consisting of four million people who made sixteen million recommendations on half a million products, and discover novel patterns: the distribution of cascade sizes is approximately heavy-tailed; cascades tend to be shallow, but occasional large bursts of propagation can occur.
Book ChapterDOI
A Unified View of Matrix Factorization Models
Ajit P. Singh,Geoffrey J. Gordon +1 more
TL;DR: A unified view of matrix factorization is presented that frames the differences among popular methods, such as NMF, Weighted SVD, E-PCA, MMMF, p LSI, pLSI-pHITS, Bregman co-clustering, and many others, in terms of a small number of modeling choices, and suggests novel generalizations of these methods.