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[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 f****ast 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]

**F****ast 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]

**F****ast 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 ]