Journal Papers



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

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples  
M. Belkin,  P. Niyogi,  V. Sindhwani
Journal of Machine Learning Research 7(Nov):2399--2434, 2006  
ps  pdf   TTI talk
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  July,2004        
pdf   link
Book Chapters

 
Newton Methods for Fast Solution of Semi-supervised  Linear SVMs
V. Sindhwani,  S.S. Keerthi
Large  Scale  Kernel  Machines, MIT  Press, 2007
ps    pdf
 
The  Geometric basis of  Semi-supervised  Learning
V. Sindhwani,  M. Belkin, P. Niyogi
Semi-supervised Learning, MIT  Press, 2006
ps   pdf   link

Conference Papers

 
An RKHS for Multi-view Learning and Manifold Co-Regularization
V. Sindhwani, D. Rosenberg
ICML 2008
ps  pdf  video

On Sparse Manifold Regularization
V. Sindhwani, O. Chapelle, S.S. Keerthi, P. Niyogi
Submitted 2008


Relational Learning with Gaussian Processes
W. Chu, V. Sindhwani, Z. Ghahramani, S. S. Keerthi
NIPS 2006
ps  pdf
Branch and Bound for Semi-supervised Support Vector Machines
O. Chapelle, V. Sindhwani, S. S. Keerthi
NIPS 2006
ps  pdf

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

Large Scale Semi-supervised Linear SVMs
V. Sindhwani, S. S. Keerthi
SIGIR 2006
ps  pdf  talk  link

Deterministic  Annealing  for  Semi-supervised  Kernel  Machines 
V. Sindhwani, S.S. Keerthi, O. Chapelle
ICML 2006
ps  pdf  talk  link

Semi-supervised Gaussian Processes
V. Sindhwani,  W. Chu,  S. S. Keerthi
IJCAI 2007
ps  pdf

Beyond  the  Point  Cloud:  from  Transductive  to  Semi-supervised  Learning 
V. Sindhwani,  P. Niyogi,  M. Belkin
ICML 2005
ps  pdf  code  talk  link
On  Manifold  Regularization 
M. Belkin,  P. Niyogi,  V. Sindhwani
Artificial  Intelligence & Statistics, Barbados, 2005
ps  pdf  link
Information  Theoretic  Feature  Crediting  in  Multiclass Support  Vector  Machines
V. Sindhwani,  P. Bhattacharya,  S. Rakshit
First  SIAM  Int. Conf. on Data Mining, Chicago, 2001

pdf  link

Workshop  Papers


SVMlin: Fast Linear SVM Solvers for Supervised and Semi-supervised Learning
V. Sindhwani
Machine Learning  Open Source Software, NIPS 2006 
 
A Co-regularization Approach to  Semi-supervised  Learning with  Multiple  Views
V. Sindhwani,  P. Niyogi,  M. Belkin
Learning with Multiple Views, ICML 2005 

Linear Manifold Regularization for Large Scale Semi-supervised  Learning
V. Sindhwani,  P. Niyogi,  M. Belkin
Learning with Partially Classified Training Data, ICML 2005   
 
 

Theses

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

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

Information Theoretic Performance Evaluation and  Feature Selection in Machine Learning
V. Sindhwani
Bachelors  Thesis, 2001