Sundararajan Sellamanickam
Sundararajan Sellamanickam
Senior Principal Researcher, Microsoft Research, Bangalore
Verified email at microsoft.com
TitleCited byYear
A dual coordinate descent method for large-scale linear SVM
CJ Hsieh, KW Chang, CJ Lin, SS Keerthi, S Sundararajan
Proceedings of the 25th international conference on Machine learning, 408-415, 2008
8432008
A sequential dual method for large scale multi-class linear SVMs
SS Keerthi, S Sundararajan, KW Chang, CJ Hsieh, CJ Lin
Proceeding of the 14th ACM SIGKDD international conference on Knowledge …, 2008
1442008
Predictive approaches for choosing hyperparameters in Gaussian processes
S Sundararajan, SS Keerthi
Neural Computation 13 (5), 1103-1118, 2001
1062001
Learning hierarchical similarity metrics
N Verma, D Mahajan, S Sellamanickam, V Nair
2012 IEEE conference on computer vision and pattern recognition, 2280-2287, 2012
882012
A joint learning framework for attribute models and object descriptions
D Mahajan, S Sellamanickam, V Nair
2011 International Conference on Computer Vision, 1227-1234, 2011
792011
Web information extraction using markov logic networks
S Satpal, S Bhadra, S Sellamanickam, R Rastogi, P Sen
Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011
392011
CRF versus SVM-struct for sequence labeling
SS Keerthi, S Sundararajan
Yahoo Research Technical Report, 2007
372007
A sequential dual method for structural SVMs
P Balamurugan, S Shevade, S Sundararajan, SS Keerthi
Proceedings of the 2011 SIAM International Conference on Data Mining, 223-234, 2011
272011
A distributed block coordinate descent method for training l 1 regularized linear classifiers
D Mahajan, SS Keerthi, S Sundararajan
The Journal of Machine Learning Research 18 (1), 3167-3201, 2017
212017
A pairwise ranking based approach to learning with positive and unlabeled examples
S Sellamanickam, P Garg, SK Selvaraj
Proceedings of the 20th ACM international conference on Information and …, 2011
202011
Large scale entity-specific resource classification
SK Selvaraj, PL Bohannon, M Muralidharan, C Yu, A Machanavajjhala, ...
US Patent 9,317,613, 2016
192016
Towards resource-elastic machine learning
S Narayanamurthy, M Weimer, D Mahajan, T Condie, S Sellamanickam, ...
NIPS 2013 BigLearn Workshop 1 (2.1), 2.3, 2013
172013
System and method for sparse gaussian process regression using predictive measures
S Sellamanickam, SK Selvaraj
US Patent App. 12/001,958, 2009
162009
A parallel SGD method with strong convergence
D Mahajan, SS Keerthi, S Sundararajan, L Bottou
arXiv preprint arXiv:1311.0636, 2013
152013
System and method for generating a classifier model for classifying web content
S Sellamanickam, SK Selvaraj
US Patent 7,949,622, 2011
152011
Learning a hierarchical monitoring system for detecting and diagnosing service issues
V Nair, A Raul, S Khanduja, V Bahirwani, Q Shao, S Sellamanickam, ...
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
142015
Semi-supervised gaussian process ordinal regression
PK Srijith, S Shevade, S Sundararajan
Joint European conference on machine learning and knowledge discovery in …, 2013
142013
Deterministic annealing for semi-supervised structured output learning
P Dhillon, S Keerthi, K Bellare, O Chapelle, S Sellamanickam
Artificial Intelligence and Statistics, 299-307, 2012
142012
An efficient distributed learning algorithm based on effective local functional approximations
D Mahajan, N Agrawal, SS Keerthi, S Sundararajan, L Bottou
arXiv preprint arXiv:1310.8418, 2013
132013
A functional approximation based distributed learning algorithm
D Mahajan, SS Keerthi, S Sundararajan, L Bottou
arXiv preprint arXiv:1310.8418, 2013
122013
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