
PUBLICATIONS
Preprints
 Harish G. Ramaswamy and Shivani Agarwal.
Convex calibration dimension for multiclass loss matrices.
arXiv:1408.2764, August 2014.
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Refereed Journal / Conference Publications
 Harikrishna Narasimhan, Rohit Vaish and Shivani Agarwal.
On the statistical consistency of plugin classifiers for nondecomposable performance measures.
In Advances in Neural Information Processing Systems (NIPS), 2014.
To appear.
 Arun Rajkumar and Shivani Agarwal.
Online decisionmaking in general combinatorial spaces.
In Advances in Neural Information Processing Systems (NIPS), 2014.
To appear.
 Aadirupa Saha, Chandrahas Dewangan, Harikrishna Narasimhan, Sriram Sampath, Shivani Agarwal.
Learning score systems for patient mortality prediction in intensive care units via orthogonal matching pursuit.
In Proceedings of the 13th International Conference on Machine Learning and Applications (ICMLA), 2014.
To appear.
 Shivani Agarwal.
Surrogate regret bounds for bipartite ranking via strongly proper losses.
Journal of Machine Learning Research, 15:16531674, 2014.
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[bibtex]
 Harish G. Ramaswamy, Balaji S.B., Shivani Agarwal and Robert C. Williamson.
On the consistency of output code based learning algorithms for multiclass learning problems.
In Proceedings of the 27th Annual Conference on Learning Theory (COLT), 2014.
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[bibtex]
 Arpit Agarwal, Harikrishna Narasimhan, Shivaram Kalyanakrishnan and Shivani Agarwal.
GEVcanonical regression for accurate binary class probability estimation when one class is rare.
In Proceedings of the 31st International Conference on Machine Learning (ICML), 2014.
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[bibtex]
 Arun Rajkumar and Shivani Agarwal.
A statistical convergence perspective of algorithms for rank aggregation
from pairwise data.
In Proceedings of the 31st International Conference on Machine Learning (ICML), 2014.
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[bibtex]
 Harikrishna Narasimhan and Shivani Agarwal.
On the relationship between binary classification, bipartite ranking, and
binary class probability estimation.
In Advances in Neural Information Processing Systems (NIPS), 2013.
[pdf]
[bibtex]
[spotlight slides]
 Harish G. Ramaswamy, Shivani Agarwal and Ambuj Tewari.
Convex calibrated surrogates for lowrank loss matrices with applications
to subset ranking losses.
In Advances in Neural Information Processing Systems (NIPS), 2013.
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[bibtex]
[spotlight slides]
 Harikrishna Narasimhan and Shivani Agarwal.
SVM_pAUC^tight: A new support vector method for optimizing partial AUC based on a tight convex upper bound.
In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2013.
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[bibtex]
 Shivani Agarwal.
Surrogate regret bounds for the area under the ROC curve via strongly proper losses.
In Proceedings of the 26th Annual Conference on Learning Theory (COLT), 2013.
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[bibtex]
 Aditya K. Menon, Harikrishna Narasimhan, Shivani Agarwal and Sanjay Chawla.
On the statistical consistency of algorithms for binary classification under class imbalance.
In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
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[bibtex]
 Harikrishna Narasimhan and Shivani Agarwal.
A structural SVM based approach for optimizing partial AUC.
In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
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[bibtex]
[supplementary material]
 Harish G. Ramaswamy and Shivani Agarwal.
Classification calibration dimension for general multiclass losses.
In Advances in Neural Information Processing Systems (NIPS), 2012.
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[bibtex]
[spotlight slides]
[longer arXiv version]
 Arun Rajkumar and Shivani Agarwal.
A differentially private stochastic gradient descent algorithm for multiparty classification.
In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.
[pdf]
 Shivani Agarwal.
The Infinite Push: A new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list.
In Proceedings of the SIAM International Conference on Data Mining (SDM), 2011.
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[bibtex]
 Shivani Agarwal.
Learning to rank on graphs.
Machine Learning, 81(3):333357, 2010.
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[bibtex]
 Shivani Agarwal, Deepak Dugar and Shiladitya Sengupta.
Ranking chemical structures for drug discovery: A new machine learning approach.
Journal of Chemical Information and Modeling, 50(5):716731, 2010.
[paper]
[bibtex]
[email me for a copy if you don't have access]
Featured as an MIT spotlight and news article.
Also featured in HPCwire, HealthCanal, PhysOrg, Science News, US News & World Report.
 Shivani Agarwal and Michael Collins.
Maximum margin ranking algorithms for information retrieval.
In Proceedings of the 32nd European Conference on Information Retrieval (ECIR), 2010.
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[bibtex]
 Shivani Agarwal and Shiladitya Sengupta.
Ranking genes by relevance to a disease.
In Proceedings of the 8th International Conference on Computational Systems Bioinformatics (CSB), 2009.
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[bibtex]
[supplementary info]
 Shivani Agarwal and Partha Niyogi.
Generalization bounds for ranking algorithms via algorithmic stability.
Journal of Machine Learning Research,
10:441474, 2009.
[pdf]
[bibtex]
 Shivani Agarwal.
Generalization bounds for some ordinal regression algorithms.
In Proceedings of the 19th International Conference on Algorithmic Learning Theory (ALT), 2008.
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[bibtex]
 Shivani Agarwal.
Ranking on graph data.
In Proceedings of the 23rd International Conference on Machine Learning (ICML), 2006.
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[bibtex]
[errata]
 Shyamsundar Rajaram and Shivani Agarwal.
Generalization bounds for kpartite ranking.
In Proceedings of the NIPS2005 Workshop on Learning to Rank, 2005.
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[bibtex]
 Shivani Agarwal, Thore Graepel, Ralf Herbrich, Sariel HarPeled and Dan Roth.
Generalization bounds for the area under the ROC curve.
Journal of Machine Learning Research,
6:393425, 2005.
[pdf]
[bibtex]
 Shivani Agarwal and Partha Niyogi.
Stability and generalization of bipartite ranking algorithms.
In Proceedings of the 18th Annual Conference on Learning Theory (COLT), 2005.
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[bibtex]
 Shivani Agarwal and Dan Roth.
Learnability of bipartite ranking functions.
In Proceedings of the 18th Annual Conference on Learning Theory (COLT), 2005.
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[bibtex]
 Shivani Agarwal, Sariel HarPeled and Dan Roth.
A uniform convergence bound for the area under the ROC curve.
In Proceedings of the 10th International Conference on
Artificial Intelligence and Statistics (AISTATS), 2005.
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[bibtex]
 Shivani Agarwal, Thore Graepel, Ralf Herbrich and Dan Roth.
A large deviation bound for the area under the ROC curve.
In Proceedings of the 18th Annual Conference on Neural Information Processing Systems (NIPS), 2004.
Published as Advances in Neural Information Processing Systems 17, pages 916, MIT Press, 2005.
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[bibtex]
 Shivani Agarwal, Aatif Awan and Dan Roth.
Learning to detect objects in images via a sparse, partbased representation.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
26(11):14751490, 2004.
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[bibtex]
 Pedro J. Moreno and Shivani Agarwal.
An experimental study of EMbased algorithms for semisupervised learning in audio classification.
In Proceedings of the ICML2003 Workshop on the Continuum from Labeled to Unlabeled Data, 2003.
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[bibtex]
 Ashutosh Garg, Shivani Agarwal and Thomas S. Huang.
Fusion of global and local information for object detection.
In Proceedings of the 16th International Conference on Pattern Recognition (ICPR), 2002.
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[bibtex]
 Shivani Agarwal and Dan Roth.
Learning a sparse representation for object detection.
In Proceedings of the 7th European Conference on Computer Vision (ECCV),
2002.
Published as Lecture Notes in Computer Science, volume 2353, pages 113130, SpringerVerlag, 2002.
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Theses / Dissertations
 Shivani Agarwal.
A study of the bipartite ranking problem in machine learning.
PhD dissertation, University of Illinois at UrbanaChampaign, 2005.
 Shivani Agarwal.
A learning approach to object detection in images using a sparse, partbased representation.
MS thesis, University of Illinois at UrbanaChampaign, 2002.
 Shivani Agarwal.
Native compilation for the CSL Lisp system.
Computer Science Tripos dissertation, University of Cambridge Computer Laboratory, 2000.
Miscellaneous
 Shivani Agarwal and Christopher M. Bishop.
An improved variational approximation for Bayesian PCA.
Technical note, Microsoft Research Cambridge, 2003.
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