Sanjiv Kumar
PhD (2005; Robotics, SCS, CMU)
Research Scientist
76, Ninth Ave
New York, NY 10011, USA
email: sanjivk AT google.com
Research Interests
Large Scale Machine Learning and Computer Vision, Graphical Models, Medical Imaging, Robotics
Teaching
EECS6898: Large-Scale Machine Learning, Fall 2010, Columbia University, New York, NY.
Tutorials
Approximate Nearest Neighbor Search (Trees and Hashes): Part-I, Part-II.
Fast Matrix Decomposition: Part-I, Part-II.
Recent
Publications [ All Publications ]
.
F. X. Yu, D. Liu, S. Kumar, T. Jebara,
and S. F. Chang
International Conference on Machine Learning (ICML), 2013.
The supplementary file with additional proofs and experiments is here.
.
Y. Gong, S. Kumar, H. Rowley,
and S. Lazebnik
IEEE Computer Vision and Pattern Recognition (CVPR), 2013.
.
Y. Gong, S. Kumar, V. Verma,
and S. Lazebnik
Advances in Neural Information Processing Systems (NIPS), 2012.
.
J. He, S. Kumar,
and S. F. Chang
International Conference on Machine Learning (ICML), 2012.
The supplementary file containing all the proofs is here.
NOTE: This is a slightly edited version of what is in the ICML proceedings.
.
W. Liu, J. Wang, Y. Mu, S. Kumar,
and S. F. Chang
International Conference on Machine Learning (ICML), 2012.
The supplementary file containing extended proofs and results is here.
.
J. Wang, S. Kumar,
and S. F. Chang
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2012.
.
S. Kumar, M. Mohri
and A. Talwalkar
Journal of Machine Learning Research (JMLR), 2012.
.
W. Liu, J. Wang, S. Kumar,
and S. F. Chang
International Conference on
Machine Learning (ICML), 2011.
.
A. Talwalkar, S. Kumar, M. Mohri
and H. Rowley
Book chapter in Manifold Learning Theory and Applications. Editors: Y. Ma and Y. Fu. CRC Press, 2011.
.
S. Kumar,
M. Mohri and A. Talwalkar
Book chapter in Ensemble Machine Learning: Theory and Applications, Springer, 2011.
Modified to correct an error in the computational complexity analysis. April 2011.
.
A. Makadia, V. Pavlovic and S. Kumar
International Journal on Computer Vision (IJCV), 2010.
.
J. Wang, S. Kumar,
and S. F. Chang
International Conference on
Machine Learning (ICML), 2010.
.
Z. Wang, M. Zhao, Y. Song, S. Kumar and B. Li
IEEE Computer Vision and Pattern Recognition (CVPR), 2010.
.
J. Wang, S. Kumar,
and S. F. Chang
IEEE Computer Vision and Pattern Recognition (CVPR), 2010.
Typo in Eq (21) corrected. June 2010.
.
S. Kumar
Book chapter in Computer Vision: Detection, Recognition and Reconstruction, Springer, 2010.
Eds. R. Cipolla, S. Battiato, and G. M. Farinella.
.
S. Kumar,
M. Mohri and A. Talwalkar
Neural Information Processing Systems
(NIPS), 2009.
Modified to correct an error in the computational complexity analysis. April 2011.
.
S. Kumar,
M. Mohri and A. Talwalkar
International Conference on
Machine Learning (ICML), 2009.
.
S. Kumar,
M. Mohri and A. Talwalkar
Twelfth International Conference on
Artificial Intelligence and Statistics (AISTATS), 2009.
.
A. Makadia,
V. Pavlovic and S. Kumar
European Conference on Computer Vision (ECCV), 2008.
.
A. Talwalkar, S. Kumar and
H. A. Rowley
IEEE Computer Vision and Pattern Recognition (CVPR), 2008.
.
M. Kim,
S. Kumar, V. Pavlovic and
H. A. Rowley
IEEE Computer Vision and Pattern Recognition (CVPR), 2008.
.
S. Kumar and
H. A. Rowley
IEEE International Conference on Computer
Vision (ICCV), 2007.
Some additional results and parts of the
video and retrieval datasets used in this work can be seen here.
·
S. Kumar and M. Hebert
International Journal of Computer Vision
(IJCV), 68(2), 179-201, 2006.
·
S. Kumar,
J. August and M. Hebert
This paper is an extended and revised version of the earlier
work presented in Snowbird Learning Workshop, 2004.
·
S. Kumar
Revised October 2005.
·
S.
Kumar and M. Hebert
A Hierarchical Field Framework for Unified
Context-Based Classification
IEEE International Conference on Computer
Vision (ICCV), 2005.
Revised October 2005.
·
C. Rother, S. Kumar, V.
Kolmogorov and A. Blake
IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR), June, 2005.
[pdf]
·
S. Kumar and M. Hebert
Snowbird Learning Workshop, Utah, 2004.
The synthetic dataset used for learning and inference
experiments can be obtained from here.
·
S. Kumar and M. Hebert
Snowbird Learning Workshop, Utah, 2004.
[pdf]
·
S. Kumar and M. Hebert
The binary denoising synthetic dataset used for
training and testing can be obtained from here.
·
B. Nabbe, S. Kumar, and
M. Hebert
In
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), October 2004.
·
S.
Kumar and M. Hebert
Discriminative Random Fields: A Discriminative
Framework for Contextual Interaction in Classification
IEEE International Conference on Computer Vision
(ICCV), 2003.
·
S. Kumar and M. Hebert
Man-Made Structure Detection in Natural Images using
a Causal Multiscale Random Field
IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR), 2003.
Some more example results
and comparisons.
The structure detection database used for training
and testing can be obtained from here.
·
S. Kumar, A. C. Loui,
and M. Hebert
An Observation-Constrained Generative Approach for
Probabilistic Classification of Image Regions
Image and Vision Computing, 21,
pp. 87-97, 2003.
A shorter version of this paper
appeared in the following workshop:
·
S. Kumar, A. C. Loui,
and M. Hebert
Probabilistic Classification of Image Regions using
an Observation-Constrained Generative Approach
ECCV Workshop on Generative Models
based Vision (GMBV), 2002.