Sanjiv Kumar

PhD (2005; Robotics, SCS, CMU)

Research Scientist

Google Research, NY

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

pSVM for Learning with Label Proportions

International Conference on Machine Learning (ICML), 2013.

[pdf]

The supplementary file with additional proofs and experiments is here.

 

.        Y. Gong, S. Kumar, H. Rowley, and S. Lazebnik

Learning Binary Codes for High-Dimensional Data Using Bilinear Projections

IEEE Computer Vision and Pattern Recognition (CVPR), 2013.

[pdf]

 

.        Y. Gong, S. Kumar, V. Verma, and S. Lazebnik

Angular Quantization-based Binary Codes for Fast Similarity Search

Advances in Neural Information Processing Systems (NIPS), 2012.

[pdf]

 

.        J. He, S. Kumar, and S. F. Chang

On the Difficulty of Nearest Neighbor Search

International Conference on Machine Learning (ICML), 2012.

[pdf]

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

Compact Hyperplane Hashing with Bilinear Functions

International Conference on Machine Learning (ICML), 2012.

[pdf]

The supplementary file containing extended proofs and results is here.

 

.        J. Wang, S. Kumar, and S. F. Chang

Semi-Supervised Hashing for Large Scale Search

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2012.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Sampling Methods for the Nystrom Method

Journal of Machine Learning Research (JMLR), 2012.

[pdf]

 

.        W. Liu, J. Wang, S. Kumar, and S. F. Chang

Hashing with Graphs

International Conference on Machine Learning (ICML), 2011.

[pdf]

 

.        A. Talwalkar, S. Kumar, M. Mohri and H. Rowley

Large-Scale Manifold Learning

Book chapter in Manifold Learning Theory and Applications. Editors: Y. Ma and Y. Fu. CRC Press, 2011.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Ensemble Nystrom

Book chapter in Ensemble Machine Learning: Theory and Applications, Springer, 2011.

[pdf]

Modified to correct an error in the computational complexity analysis. April 2011.

 

.        A. Makadia, V. Pavlovic and S. Kumar

Baselines for Image Annotation

International Journal on Computer Vision (IJCV), 2010.

[pdf]

 

.        J. Wang, S. Kumar, and S. F. Chang

Sequential Projection Learning for Hashing with Compact Codes

International Conference on Machine Learning (ICML), 2010.

[pdf]

 

.        Z. Wang, M. Zhao, Y. Song, S. Kumar and B. Li

YouTubeCat: Learning to Categorize Wild Web Videos

IEEE Computer Vision and Pattern Recognition (CVPR), 2010.

[pdf]

 

.        J. Wang, S. Kumar, and S. F. Chang

Semi-Supervised Hashing for Scalable Image Retrieval

IEEE Computer Vision and Pattern Recognition (CVPR), 2010.

[pdf]

Typo in Eq (21) corrected. June 2010.

 

.        S. Kumar

Discriminative Graphical Models for Context-Based Classification

Book chapter in Computer Vision: Detection, Recognition and Reconstruction, Springer, 2010.

Eds. R. Cipolla, S. Battiato, and G. M. Farinella.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Ensemble Nystrom Method

Neural Information Processing Systems (NIPS), 2009.

[pdf]

Modified to correct an error in the computational complexity analysis. April 2011.

 

.        S. Kumar, M. Mohri and A. Talwalkar

On Sampling-based Approximate Spectral Decomposition

International Conference on Machine Learning (ICML), 2009.

[pdf]

 

.        S. Kumar, M. Mohri and A. Talwalkar

Sampling Techniques for the Nystrom Method

Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 2009.

[pdf]

 

.        A. Makadia, V. Pavlovic and S. Kumar

A New Baseline for Image Annotation

European Conference on Computer Vision (ECCV), 2008.

[pdf]

 

.        A. Talwalkar, S. Kumar and H. A. Rowley

Large-Scale Manifold Learning

IEEE Computer Vision and Pattern Recognition (CVPR), 2008.

[pdf]

 

.        M. Kim, S. Kumar, V. Pavlovic and H. A. Rowley

Face Tracking and Recognition with Visual Constraints in Real-World Videos

IEEE Computer Vision and Pattern Recognition (CVPR), 2008.

[pdf]

 

.        S. Kumar and H. A. Rowley

Classification of Weakly-Labeled Data with Partial Equivalence Relations

IEEE International Conference on Computer Vision (ICCV), 2007.

[pdf]

Some additional results and parts of the video and retrieval datasets used in this work can be seen here.

 

        S. Kumar and M. Hebert

Discriminative Random Fields

International Journal of Computer Vision (IJCV), 68(2), 179-201, 2006.

[pdf]

 

        S. Kumar, J. August and M. Hebert

Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study

Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2005.

[pdf]

This paper is an extended and revised version of the earlier work presented in Snowbird Learning Workshop, 2004.

 

        S. Kumar

Models for Learning Spatial Interactions in Natural Images for Context-Based Classification

PhD Thesis, The Robotics Institute, School of Computer Science, Carnegie Mellon University, September 2005.

[pdf] [ps]

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.

[pdf] [ps]

Revised October 2005.

 

        C. Rother, S. Kumar, V. Kolmogorov and A. Blake

Digital Tapestry

IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), June, 2005.

[pdf]

 

        S. Kumar and M. Hebert

Approximate Parameter Learning in Discriminative Fields

Snowbird Learning Workshop, Utah, 2004.

[pdf] [ps]

The synthetic dataset used for learning and inference experiments can be obtained from here.

 

        S. Kumar and M. Hebert

Multiclass Discriminative Fields for Parts-Based Object Detection

Snowbird Learning Workshop, Utah, 2004.

[pdf]

 

        S. Kumar and M. Hebert

Discriminative Fields for Modeling Spatial Dependencies in Natural Images

Advances in Neural Information Processing Systems, NIPS 16, 2004.

[pdf] [ps]

The binary denoising synthetic dataset used for training and testing can be obtained from here.

 

        B. Nabbe, S. Kumar, and M. Hebert

      Path Planning with Hallucinated Worlds

      In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 2004.

      [pdf]

           

        S. Kumar and M. Hebert

Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification

IEEE International Conference on Computer Vision (ICCV), 2003.

[pdf] [ps]

 

        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.

[pdf]

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.

[pdf] 

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.

[pdf]