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A fast algorithm for learning a ranking function from large scale data sets Vikas C. Raykar, Ramani Duraiswami, and Balaji Krishnapuram, accepted for future publication in IEEE Transactions on Pattern Analysis and Machine Intelligence. [preprint]
The Improved Fast Gauss Transform with applications to machine learning Vikas C. Raykar and Ramani Duraiswami, To appear in Large Scale Kernel Machines L. Bottou, O. Chapelle, D. Decoste, and J. Weston (Eds), MIT Press 2006. [chapter]
On Ranking in Survival Analysis: Bounds on the Concordance Index Vikas C. Raykar, Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, and Philippe Lambin. In Advances in Neural Information Processing Systems, vol. 20, pp. – , 2008. [paper] [slides] [spotlight slide] [bib]
A fast algorithm for learning large scale preference relations. Vikas C. Raykar, Ramani Duraiswami, and Balaji Krishnapuram, In Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, San Juan, Peurto Rico, March 2007, pp. 385-392. [paper] [slides] [code] [bib] [ More details can be found in CS-TR-4848 ] [oral presentation]
Fast optimal bandwidth selection for kernel density estimation. Vikas C. Raykar and Ramani Duraiswami, In Proceedings of the sixth SIAM International Conference on Data Mining, Bethesda, April 2006, pp. 524-528. [paper] [brief slides] [code] [bib] [ Detailed version available as CS-TR-4774 ]
The
manifolds of spatial hearing
Ramani Duraiswami and
Vikas C. Raykar, In Proceedings of International
Conference on Acoustics, Speech and Signal Processing (ICASSP
2005), Philadelphia, March 2005, vol. III, pp. 285-288
[
Slides ]
Fast large scale Gaussian process regression using approximate matrix-vector products. Vikas C. Raykar and Ramani Duraiswami, Presented at the Learning workshop 2007, San Juan, Peurto Rico, March 2007. [abstract] [detailed paper] [slides]
On the manifolds of spatial hearing. Vikas C. Raykar and Ramani Duraiswami, Presented at the NIPS 2006 workshop on Novel Applications of Dimensionality Reduction. [abstract] [slides]
The improved fast Gauss Transform with applications to machine learning. Vikas C. Raykar and Ramani Duraiswami, Presented at the NIPS 2005 workshop on Large scale kernel machines. [slides] [code] [video]
Fast weighted summation of erfc functions. Vikas C. Raykar, R. Duraiswami, and B. Krishnapuram, CS-TR-4848, Department of computer science, University of Maryland, CollegePark. [abstract] [TR] [slides] [code] [bib]
Very fast optimal bandwidth selection for univariate kernel density estimation. Vikas C. Raykar and R. Duraiswami, CS-TR-4774, Department of computer science, University of Maryland, CollegePark. [abstract] [TR] [slides] [code] [bib]
Fast computation of sums of Gaussians in high dimensions. Vikas C. Raykar, C. Yang, R. Duraiswami, and N. Gumerov, CS-TR-4767, Department of computer science, University of Maryland, Collegepark. [abstract] [TR] [slides] [code] [bib]
THESIS
Scalable machine learning for massive datasets: Fast summation algorithms Doctoral dissertation, Department of computer science, University of Maryland College Park, March 2007 [ Research Summary ] [ Thesis ] [ Slides ]