[HOME] [EDUCATION] [PUBLICATIONS] [RESEARCH] [SOFTWARE]

The copyrights of publications are with the respective publishers. The papers are being reproduced here for timely dissemination of scholarly information.

 [ALL] [MACHINE LEARNING/STATISTICS] [MEDICAL IMAGING] [SIGNAL PROCESSING] [PATENTS]

Learning from crowds Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Valadez, Charles Florin, Luca Bogoni, and Linda Moy, Journal of Machine Learning Research, 11(Apr):1297−1322, 2010 [abstract] [paper] [bib]

Supervised Learning from Multiple Experts: Whom to trust when everyone lies a bit Vikas C. Raykar, Shipeng Yu, Linda Zhao, Anna Jerebko, Charles Florin, Gerardo Valadez, Luca Bogoni, and Linda Moy, In Proceedings of the 26th International Conference on Machine Learning (ICML 2009), pp.889-896, Montreal, June 2009.  [paper] [discussion] [slides] [bib] 

Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer Vikas C. Raykar, Balaji Krishnapuram, Jinbo Bi, Murat Dundar, and R. Bharat Rao, In Proceedings of the 25th International Conference on Machine Learning (ICML 2008), pp.808-815, Helsinki, July 2008.  [paper] [slides] [bib]

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 (NIPS 2007), vol. 20, pp. 1209–1216 , 2008.  [paper] [slides] [spotlight slide] [bib]

- High Dimensional Classification/Feature Selection -

Empirical bayesian thresholding for sparse signals using mixture loss functions Vikas C. Raykar, and Linda H. Zhao To appear in Statistica Sinica [preprint] 

Nonparametric prior for adaptive sparsity Vikas C. Raykar and Linda H. Zhao, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, JMLR: W&CP 9, pp.629-636, Chia Laguna, Sardinia, Italy, May 13-15, 2010 [abstract] [paper] [slides] [bib]

- Scalable machine learning algorithms -

Designing efficient cascaded classifiers: Tradeoff between accuracy and cost Vikas C. Raykar, Balaji Krishnapuram, and Shipeng Yu, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'10), pp.853-860, Washington DC, July 2010. [abstract] [paper] [slides] [bib] [acceptance rate 17%] [oral presentation] 

Fast Computation of Kernel Estimators Vikas C. Raykar, Ramani Duraiswami, and Linda H. Zhao, Journal of Computational and Graphical Statistics. March 2010, Vol. 19, No. 1: 205-220 [abstract] [paper] [bib]

A fast algorithm for learning a ranking function from large scale data sets  Vikas C. Raykar, Ramani Duraiswami, and Balaji Krishnapuram, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1158-1170, July 2008. [paper]   

Automatic online tuning for fast Gaussian summation Vlad I. Morariu, Balaji V. Srinivasan, Vikas C. Raykar, Ramani Duraiswami, and Larry Davis, In Advances in Neural Information Processing Systems (NIPS 2008), vol. 21, pp.1113-1120, 2009.  [paper] [spotlight slide] [bib] [code]

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 Improved Fast Gauss Transform with applications to machine learning  Vikas C. Raykar, and Ramani Duraiswami, In Large Scale Kernel Machines  L. Bottou, O. Chapelle, D. Decoste, and J. Weston (Eds), MIT Press 2006. [chapter]

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]

Efficient Kriging via Fast Matrix-Vector Products Nargess Memarsadeghi, Vikas C. Raykar, Ramani Duraiswami, and David M. Mount. In IEEE Aerospace Conference, Big Sky, Montana, March 2008.  [paper]

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]

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]  

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 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]

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 ]

-  Applications -

A Multiple Instance Learning Approach toward Optimal Classification of Pathology Slides  Murat Dundar, Sunil Badve, Vikas C. Raykar, Rohit Jain, Olcay Sertel, and Metin Gurcan, Proceedings of 20th International Conference on Pattern Recognition [preprint] [bib] [acceptance rate 18%] [Best Scientific Paper Award in Bioinformatics and Biomedical Applications Track]

Mining Medical Images  R. Bharat Rao, Glenn Fung,  Balaji Krishnapuram,  Jinbo Bi,  Murat Dundar, Vikas C. Raykar, Shipeng Yu,  Sriram Krishnan, Xiang Zhou, Arun Krishnan, Marcos Salganicoff, Luca Bogoni, Matthias Wolf, Anna Jerebko, and Jonathan Stoeckel, In Proceedings of the Third Workshop on Data Mining Case Studies and Practice Prize, Fifteenth Annual SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), Paris, June 2009.  [paper] [bib] [First place prize winner]

Polyhedral Classifier for Target Detection A Case Study: Colorectal Cancer Murat Dundar, Matthias Wolf, Sarang Lakare, Marcos Salganicoff, and Vikas C. Raykar, In Proceedings of the 25th International Conference on Machine Learning (ICML 2008), pp.288-295, Helsinki, July 2008.  [paper] [slides] [bib]

Multiple instance learning improves CAD detection of masses in digital mammography Balaji Krishnapuram, Jonathan Stoeckel, Vikas C. Raykar, R. Bharat Rao, Philippe Bamberger, Eli Ratner, Nicolas Merlet, Inna stainvas, Menahem Abramov, and Alexandra Manevitch, In Proceedings of the 9th international workshop on Digital Mammography (IWDM 2008), pp.350-357, Tucson, AZ, July 2008.  [paper] [slides] [bib] [oral presentation]

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 ]