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

Big Data, Large Scale Machine Learning, 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 ]

. Ian E. H. Yen, S. Kale, F. X. Yu, D. Holtmann-Rice, S. Kumar, P. Ravikumar

Loss Decomposition for Fast Learning in Large Output Spaces

International Conference on Machine Learning (ICML), 2018.

[pdf]

. S. Reddi, S. Kale, S. Kumar

On the Convergence of Adam and Beyond [best paper award]

International Conference on Learning Representations (ICLR), 2018.

[pdf]

. S. Si, S. Kumar, Y. Li

Nonlinear Online Learning with Adaptive Nystrom Approximation

arXiv:1802.07887, 2018.

[pdf]

. X. Wu, R. Guo, A. T. Suresh, S. Kumar, D. Holtmann-Rice, D. Simcha, F. X. Yu

Multiscale Quantization for Fast Similarity Search

Neural Information Processing Systems (NIPS), 2017.

[pdf]

. B. Dai, R. Guo, S. Kumar, N. He, L. Song

Stochastic Generative Hashing

International Conference on Machine Learning (ICML), 2017.

[pdf]

. A. T. Suresh, F. X. Yu, S. Kumar, H. B. McMahan

Distributed Mean Estimation with Limited Communication

International Conference on Machine Learning (ICML), 2017.

[pdf]

. X. Zhang, F. X. Yu, S. Kumar, S. F. Chang

Learning Spread-out Local Feature Descriptors

International Conference on Computer Vision (ICCV), 2017.

[pdf]

. K. Zhong, R. Guo, S. Kumar, B. Yan, D. Simcha, I. S. Dhillon

Fast Classification with Binary Prototypes

International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

[pdf]

. F. X. Yu, A. T. Suresh, K. Choromanski, D. Holtmann-Rice, S. Kumar

Orthogonal Random Features

Neural Information Processing Systems (NIPS), 2016.

[pdf]

. A. Choromanska, K. Choromanski, M. Bojarski, T. Jebara, S. Kumar, Y. LeCun

Binary Embeddings with Structured Hash Projections

International Conference on Machine Learning (ICML), 2016.

[pdf]

. R. Guo, S. Kumar, K. Choromanski, and D. Simcha

Quantization based Fast Inner Product Search

International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.

[pdf]

Previous Arxiv version: arXiv:1509.01469, 2015. [pdf]

. J. Pennington, F. X. Yu, S. Kumar

Spherical Random Features for Polynomial Kernels

Neural Information Processing Systems (NIPS), 2015.

[pdf]

. V. Sindhwani, T. Sainath, S. Kumar

Structured Transforms for Small-Footprint Deep Learning

Neural Information Processing Systems (NIPS), 2015.

[pdf]

. X. Zhang, F. X. Yu, Ruiqi Guo, S. Kumar, S. Wang, S.-F. Chang

Fast Orthogonal Projection Based on Kronecker Product

International Conference on Computer Vision (ICCV), 2015.

[pdf]

. Y. Cheng, F. X. Yu, R. S. Feris, S. Kumar, A. Choudhary, and S. F. Chang

An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections

International Conference on Computer Vision (ICCV), 2015.

[pdf]

Previous arXiv version: arXiv:1502.03436v1, 2015. [pdf]

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

Learning to Hash for Indexing Big Data - A Survey

arXiv:1509.05472v1, 2015.

[pdf]

. K. Choromanski, S. Kumar, and X. Liu

Fast Online Clustering with Randomized Skeleton Sets

arXiv:1506.03425v1, 2015.

[pdf]

. F. X. Yu, S. Kumar, H. Rowley, and S. F. Chang

Compact Nonlinear Maps and Circulant Extensions

arXiv:1503.03893v1, 2015.

[pdf]

. F. X. Yu, Y. Gong, and S. Kumar

Fast Binary Embedding for High-Dimensional Data

Book Chapter, Multimedia Data Mining and Analytics, 2015.

[pdf]

. W. Liu, C. Mu, S. Kumar, and S. F. Chang

Discrete Graph Hashing

Neural Information Processing Systems (NIPS), 2014.

[pdf]

Supplementary material can be found here.

. F. X. Yu, S. Kumar, Y. Gong, and S. F. Chang

Circulant Binary Embedding

International Conference on Machine Learning (ICML), 2014.

[pdf]

Matlab code can be found here.

. 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]

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

Large-scale SVD and Manifold Learning

Journal of Machine Learning Research (JMLR), 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]