Publications


2009


A Family of Non-negative Matrix Factorizations for One-Class Collaborative Filtering
V. Sindhwani, S.S. Bucak, J. Hu, A. Mojsilovic
Recommender based Industrial Applications Workshop
ACM Recommender Systems, 2009
pdf 

Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision
V. Sindhwani, P. Melville, R. Lawrence
International Conference on Machine Learning (ICML), 2009
pdf 

Sparse Least Squares Methods in the Parallel Machine Learning (PML) Framework
R. Natarajan, V.Sindhwani, S. Tatikonda
Large Scale Data Mining: Theory and Applications Workshop
International Conference on Data Mining
(ICDM), 2009
pdf 

A Kernel for Semi-supervised Learning with Multi-view Point Cloud Regularization
D. Rosenberg, V. Sindhwani, P. Bartlett, P. Niyogi
IEEE Signal Processing Magazine, 2009 (to appear)
pdf 

A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge
T. Li, Y. Zhang and V. Sindhwani
Association for Computational Linguistics (ACL), 2009
pdf 

Winning the KDD Cup Orange Challenge with Ensemble Selection
A. Niculescu-Mizil, C. Perlich, G. Swirszcz, V. Sindhwani, Y. Liu, P. Melville, D. Wang, J. Xiao, J. Hu, M. Singh, W. X. Shang, Y.F. Zhu
Journal of Machine Learning Research (JMLR proceeding series), 2009
pdf 

Social Media Analytics: Channeling the Power of the Blogosphere for Marketing Insight.
P. Melville, V. Sindhwani, R. Lawrence
Workshop on Information in Networks (WIN), 2009
pdf 

Knowledge Transformation for Cross-domain Sentiment Classification
T. Li, V. Sindhwani, C. Ding, Y. Zhang
32nd Annual ACM SIGIR (Poster), 2009
link 

Active Dual Supervision: Reducing the Cost of Annotating Examples and Features
P. Melville and V. Sindhwani
NAACL HLT 2009 Workshop on Active Learning for NLP, 2009
pdf 

Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria.
P. Hsueh, P. Melville, V. Sindhwani
NAACL HLT 2009 Workshop on Active Learning for NLP, 2009
pdf 

Leveraging Social Networks for Corporate Staffing and Expert Recommendation
V. Chenthamarakshan, K. Dey, J. Hu, A. Mojsilovic, W. Riddle, V. Sindhwani
IBM Journal of Research and Development, 2009
to appear 

2008

Regularized Co-Clustering With Dual Supervision
V. Sindhwani, J. Hu, A. Mojsilovic
Neural Information Processing Systems (NIPS), 2008
pdf 

Document-Word Co-Regularization for Semi-supervised Sentiment Analysis
V. Sindhwani, P. Melville
International Conference on Data Mining (ICDM), 2008
pdf techreport 

An RKHS for Multi-view Learning and Manifold Co-Regularization
V. Sindhwani, D. Rosenberg
International Conference on Machine Learning (ICML), 2008
ps pdf video 

Optimization Techniques for Semi-supervised Support Vector Machines
O. Chapelle, V. Sindhwani, S. S. Keerthi
Journal of Machine Learning Research 9(Feb):203--233, 2008
pdf 


2007

On Semi-supervised Kernel Methods
V. Sindhwani
Doctoral Thesis , University of Chicago, 2007
pdf

Newton Methods for Fast Solution of Semi-supervised Linear SVMs
V. Sindhwani, S.S. Keerthi
Large Scale Kernel Machines MIT Press (Book Chapter), 2007
ps pdf 

Semi-supervised Gaussian Processes
V. Sindhwani, W. Chu, S. S. Keerthi
International Joint Conference on Artificial Intelligence (IJCAI), 2007
ps pdf 


2006

An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models
S. S. Keerthi, V. Sindhwani, O. Chapelle
Neural Information Processing Systems (NIPS), 2006
ps pdf 

Relational Learning with Gaussian Processes
W. Chu, V. Sindhwani, Z. Ghahramani, S. S. Keerthi
Neural Information Processing Systems (NIPS), 2006
ps pdf 

Branch and Bound for Semi-supervised Support Vector Machines
O. Chapelle, V. Sindhwani, S. S. Keerthi
Neural Information Processing Systems (NIPS), 2006
ps pdf 

Deterministic Annealing for Semi-supervised Kernel Machines
V. Sindhwani, S.S. Keerthi, O. Chapelle
International Conference on Machine Learning (ICML), 2006
ps pdf talk 

Large Scale Semi-supervised Linear SVMs
V. Sindhwani, S.S. Keerthi
29th Annual International ACM SIGIR, 2006
ps pdf talk techreport 

The Geometric Basis of Semi-supervised Learning
V. Sindhwani, M. Belkin, P. Niyogi
Semi-supervised Learning MIT Press (Book Chapter), 2006
ps pdf link 

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
M. Belkin, P. Niyogi, V. Sindhwani
Journal of Machine Learning Research (JMLR), 2006
ps pdf talk 

SVMlin: Fast Linear SVM Solvers for Supervised and Semi-supervised Learning
V. Sindhwani, P. Niyogi, M. Belkin
Workshop on Machine Learning Open Source Software, Neural Information Processing Systems (NIPS), 2006
link


2005

Beyond the Point Cloud: from Transductive to Semi-supervised Learning
V. Sindhwani, P. Niyogi, M. Belkin
International Conference on Machine Learning (ICML), 2005
ps pdf code talk 

A Co-regularization Approach to Semi-supervised Learning with Multiple Views
V. Sindhwani, P. Niyogi, M. Belkin
Workshop on Learning with Multiple Views, International Conference on Machine Learning (ICML), 2005
pdf link 

On Manifold Regularization
M. Belkin, P. Niyogi, V. Sindhwani
Artificial Intelligence and Statistics (AISTATS), 2005
ps pdf link 

Linear Manifold Regularization for Large Scale Semi-supervised Learning
V. Sindhwani, P. Niyogi, M. Belkin
Workshop on Learning with Partially Classified Training Data, International Conference on Machine Learning (ICML), 2005
pdf link 


2004

Kernel Machines for Semi-supervised Learning
V. Sindhwani
Masters Thesis , 2004
University of Chicago 

Feature Selection in MLPs and SVMs Based On Maximum Output Information
V. Sindhwani, S. Rakshit, D. Deodhare, D. Erdogmus, J.Principe, P.Niyogi
IEEE Trans. on Neural Networks,,V.15,N.4, 2004
pdf 


Earlier

Information Theoretic Performance Evaluation and Feature Selection in Machine Learning
V. Sindhwani
Bachelors Thesis , 2001
Indian Institute of Technology, Bombay 

Information Theoretic Feature Crediting in Multiclass Support Vector Machines
V. Sindhwani, P. Bhattacharya, S. Rakshit
First Siam International Conference on Data Mining (SDM), 2001
pdf