List of Publications

Machine Learning and Computer Vision

• Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosef, Sashank J. Reddi, Sanjiv Kumar
  On the Inductive Bias of Stacking Towards Improving Reasoning
  Neural Information Processing Systems (NeurIPS), 2024.
  [pdf]
  
  
• Taehyeon Kim, Ananda Theertha Suresh, Kishore Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton
  Accelerating Blockwise Parallel Language Models with Draft Refinement
  Neural Information Processing Systems (NeurIPS), 2024.
  [pdf]
  
  
• Khashayar Gatmiry, Nikunj Saunshi, Sashank Reddi, Stefanie Jegelka, Sanjiv Kumar
  Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning?
  International Conference on Machine Learning (ICML), 2024.
  [pdf]
  
  
• Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore Papineni, Sanjiv Kumar, Andrej Risteski
  Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
  International Conference on Machine Learning (ICML), 2024.
  [pdf]
  
  
• Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Wittawat Jitkrittum, Veeranjaneyulu Sadhanala, Sadeep Jayasumana, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar
  USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval
  International Conference on Machine Learning (ICML), 2024.
  [pdf]
  
  
• Aishwarya P S, Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
  Tandem Transformers for Inference Efficient LLMs
  International Conference on Machine Learning (ICML), 2024.
  [pdf]
  
  
• Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan
  Think Before You Speak: Training Language Models with Pause Tokens
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar
  Plugin Estimators for Selective Classification with Out-Of-Distribution Detection
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar
  Language Model Cascades: Token-Level Uncertainty and Beyond
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar
  Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal
  DistillSpec: Improving Speculative Decoding Via Knowledge Distillation
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli
  Functional Interpolation for Relative Positions Improves Long Context Transformers
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar
  On Bias-Variance Alignment in Deep Models
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum Neha Gupta, Sanjiv Kumar
  Learning to Reject Meets Long-Tail Learning
  International Conference on Learning Representations (ICLR), 2024.
  [pdf]
  
  
• V. Nagarajan, A. K. Menon, S. Bhojanapalli, H. Mobahi, and S. Kumar
  On Student-Teacher Deviations in Distillation: Does It Pay to Disobey?
  Neural Information Processing Systems (NeurIPS), 2023.
  [pdf]
  
  
• Z. Yang, M. Lukasik, V. Nagarajan, Z. Li, A. S. Rawat, M. Zaheer, A. K. Menon, and S. Kumar
  ResMem: Learn What You Can and Memorize the Rest
  Neural Information Processing Systems (NeurIPS), 2023.
  [pdf]
  
  
• P. Sun, D. Simcha, D. Dopson, R. Guo, and S. Kumar
  SOAR: Improved Indexing for Approximate Nearest Neighbor Search
  Neural Information Processing Systems (NeurIPS), 2023.
  [pdf]
  
  
• W. Jitkrittum, N. Gupta, A. K. Menon, H. Narasimhan A. S. Rawat, and S. Kumar
  When Does Confidence-Based Cascade Deferral Suffice?
  Neural Information Processing Systems (NeurIPS), 2023.
  [pdf]
  
  
• S. J. Reddi, S. Miryoosefi, S. Karp, S. Krishnan, S. Kale, S. Kim, and S. Kumar
  Efficient Training of Language Models using Few-Shot Learning
  International Conference on Machine Learning (ICML), 2023.
  [pdf]
  
  
• G. Citovsky, G. DeSalvo, S. Kumar, S. Ramalingam, A. Rostamizadeh, and Y. Wang
  Leveraging Importance Weights in Subset Selection
  International Conference on Learning Representations (ICLR), 2023.
  [pdf]
  
  
• Z. Li, C. You, S. Bhojanapalli, D. Li, A. S. Rawat, S. J. Reddi, K. Ye, F. Chern, F. Yu, R. Guo, and S. Kumar
  The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers
  International Conference on Learning Representations (ICLR), 2023.
  [pdf]
  
  
• M. Zaheer, A. S. Rawat, S. Kim, C. You, H. Jain, A. Veit, R. Fergus, and S. Kumar
  Teacher Guided Training: An Efficient Framework for Knowledge Transfer
  International Conference on Learning Representations (ICLR), 2023.
  [pdf]
  
  
• H. Harutyunyan, A. S. Rawat, A. K. Menon, S. Kim, and S. Kumar
  Supervision Complexity and its Role in Knowledge Distillation
  International Conference on Learning Representations (ICLR), 2023.
  [pdf]
  
  
• P. Sun, R. Guo, and S. Kumar
  Automating Nearest Neighbor Search Configuration with Constrained Optimization
  International Conference on Learning Representations (ICLR), 2023.
  [pdf]
  
  
• S. Si, F. Yu, A. S. Rawat, C.-J. Hsieh, and S. Kumar
  Serving Graph Compression for Graph Neural Networks
  International Conference on Learning Representations (ICLR), 2023.
  [pdf]
  
  
• Z. Li, R. Guo, and S. Kumar
  Decoupled Context Processing for Context Augmented Language Modeling
  Neural Information Processing Systems (NeurIPS), 2022.
  [pdf]
  
  
• H. Narasimhan, W. Jitkrittum, A. K. Menon, A. S. Rawat, and S. Kumar
  Post-hoc Estimators for Learning to Defer to an Expert
  Neural Information Processing Systems (NeurIPS), 2022.
  [pdf]
  
  
• F. Chern, B. Hechtman, A. Davis, R. Guo, D. Majnemer, and S. Kumar
  TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
  Neural Information Processing Systems (NeurIPS), 2022.
  [pdf]
  
  
• A. K. Menon, S. Jayasumana, A. S. Rawat, S. Kim, S. J. Reddi, and S. Kumar
  In Defense of Dual-Encoders for Neural Ranking
  International Conference on Machine Learning (ICML), 2022.
  [pdf]
  
  
• Z. Li, S. Bhojanapalli, M. Zaheer, S. J. Reddi and S. Kumar
  Robust Training of Neural Networks Using Scale Invariant Architectures
  International Conference on Machine Learning (ICML), 2022.
  [pdf]
  
  
• E. M. Lindgren, S. J, Reddi, R. Guo and S. Kumar
  Efficient Training of Retrieval Models Using Negative Cache
  Neural Information Processing Systems (NeurIPS), 2021.
  [pdf]
  
  
• G. Citovsky, G. DeSalvo, C. Gentile, L. Karydas, A. Rajagopalan, A. Rostamizadeh and S. Kumar
  Batch Active Learning at Scale
  Neural Information Processing Systems (NeurIPS), 2021.
  [pdf]
  
  
• A. K. Menon, A. S. Rawat, S. J. Reddi, S. Kim, and S. Kumar
  A Statistical Perspective on Distillation
  International Conference on Machine Learning (ICML), 2021.
  [pdf]
  
  
• A. S. Rawat, A. K. Menon, W. Jitkrittum, S. Jayasumana, F. X. Yu, S. J. Reddi, and S. Kumar
  Disentangling Labeling and Sampling Bias for Learning in Large-output Spaces
  International Conference on Machine Learning (ICML), 2021.
  [pdf]
  
  
• S. J. Reddi, R. K. Pasumarthi, A. K. Menon, A. S. Rawat, F. Yu, S. Kim, A. Veit, and S. Kumar
  RankDistil: Knowledge Distillation for Ranking
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2021.
  [pdf]
  
  
• A. K. Menon, A. S. Rawat, and S. Kumar
  Overparameterisation and Worst-case Generalisation: Friend or Foe?
  International Conference on Learning Representations (ICLR), 2021.
  [pdf]
  
  
• S. J. Reddi, Z. Charles, M. Zaheer, Z. Garrett, K. Rush, J. Konecný, S. Kumar, and H. B. McMahan
  Adaptive Federated Optimization
  International Conference on Learning Representations (ICLR), 2021.
  [pdf]
  
  
• A. K. Menon, S. Jayasumana, A. S. Rawat, H. Jain A. Veit and S. Kumar
  Long-tail Learning via Logit Adjustment
  International Conference on Learning Representations (ICLR), 2021.
  [pdf]
  
  
• C.-Y. Hsieh, C.-K. Yeh, X. Liu, P. Ravikumar, S. Kim, S. Kumar, and C.-J. Hsieh
  Evaluations and Methods for Explanation Through Robustness Analysis
  International Conference on Learning Representations (ICLR), 2021.
  [pdf]
  
  
• J. Zhang, A. K. Menon, A. Veit, S. Bhojanapalli, S. Kumar, and S. Sra
  Coping With Label Shift via Distributionally Robust Optimisation
  International Conference on Learning Representations (ICLR), 2021.
  [pdf]
  
  
• C. Yun, Y.-W. Chang, S. Bhojanapalli, A. S. Rawat, S. Reddi, and S. Kumar
  O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers
  Neural Information Processing Systems (NeurIPS), 2020.
  [pdf]
  
  
• J. Zhang, S. P. Karimireddy, A. Veit, S. Kim, S. Reddi, and S. Kumar
  Why are Adaptive Methods Good for Attention Models?
  Neural Information Processing Systems (NeurIPS), 2020.
  [pdf]
  
  
• M. Weber, M. Zaheer, A. S. Rawat, A. Menon, and S. Kumar
  Robust Large-Margin Learning in Hyperbolic Space
  Neural Information Processing Systems (NeurIPS), 2020.
  [pdf]
  
  
• Y. Liu, A. T. Suresh, F. Yu, S. Kumar, and M. Riley
  Learning Discrete Distributions: User vs Item-Level Privacy
  Neural Information Processing Systems (NeurIPS), 2020.
  [pdf]
  
  
• H. Chen, S. Si, Y. Li, C. Chelba, S. Kumar, D. Boning, and C.-J. Hsieh
  Multi-Stage Influence Function
  Neural Information Processing Systems (NeurIPS), 2020.
  [pdf]
  
  
• S. Bhojanapalli, C. Yun, A. S. Rawat, S. Reddi, and S. Kumar
  Low-Rank Bottleneck in Multi-head Attention Models
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
  
  
• M. Lukasik, S. Bhojanapalli, A. K. Menon, and S. Kumar
  Does Label Smoothing Mitigate Label Noise?
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
  
  
• R. Guo, P. Sun, E. Lindgren, Q. Geng, D. Simcha, F. Chern and S. Kumar
  Accelerating Large-Scale Inference with Anisotropic Vector Quantization
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
  
  
• F. X. Yu, A. S. Rawat, A. K. Menon, and S. Kumar
  Federated Learning with Only Positive Labels
  International Conference on Machine Learning (ICML), 2020.
  [pdf]
  
  
• Y. You, J. Li, S. Reddi, J. Hseu, S. Kumar, S. Bhojanapalli, X. Song, J. Demmel, K. Keutzer, and C.-J. Hsieh
  Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes
  International Conference on Learning Representations (ICLR), 2020.
  [pdf]
  
  
• C. Yun, S. Bhojanapalli, A. S. Rawat, S. J. Reddi, and S. Kumar
  Are Transformers Universal Approximators of Sequence-to-Sequence Functions?
  International Conference on Learning Representations (ICLR), 2020.
  [pdf]
  
  
• A. K. Menon, A. S. Rawat, S. J. Reddi, and S. Kumar
  Can Gradient Clipping Mitigate Label Noise?
  International Conference on Learning Representations (ICLR), 2020.
  [pdf]
  
  
• W.-C. Chang, F. Yu, Y.-W. Chang, and S. Kumar
  Pre-training Tasks for Embedding-based Large-scale Retrieval
  International Conference on Learning Representations (ICLR), 2020.
  [pdf]
  
  
• Y. Ruan, Y. Xiong, S. Reddi, S. Kumar, C.-J. Hsieh
  Learning to Learn by Zeroth-Order Oracle
  International Conference on Learning Representations (ICLR), 2020.
  [pdf]
  
  
• C.-J. Hsieh, Q. Cao, S. Kumar, S. Si, T. Xiao, and X. Liu
  How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework
  International Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  [pdf]
  
  
• A. K. Menon, A. S. Rawat, S. J. Reddi, and S. Kumar
  Multilabel reductions: what is my loss optimising?
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
  
  
• C. Guo, A. Mousavi, X. Wu, D. Holtmann-Rice, S. Kale, S. Reddi and S. Kumar
  Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
  
  
• A. S. Rawat, J. Chen, F. Yu, A. T. Suresh, and S. Kumar
  Sampled Softmax with Random Fourier Features
  Neural Information Processing Systems (NeurIPS), 2019.
  [pdf]
  
  
• M. Staib, S. Reddi, S. Kale, S. Kumar, and S. Sra
  Escaping Saddle Points with Adaptive Gradient Methods
  International Conference on Machine Learning (ICML), 2019.
  [pdf]
  
  
• S. Wu, A. G. Dimakis, S. Sanghavi, F. X. Yu, D. Holtmann-Rice, D. Storcheus, A. Rostamizadeh, and S. Kumar
  Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
  International Conference on Machine Learning (ICML), 2019.
  [pdf]
  
  
• P.-H. (Patrick) Chen, S. Si, S. Kumar, Y. Li, and C.-J. Hsieh
  Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
  International Conference on Learning Representations (ICLR), 2019.
  [pdf]
  
  
• S. Reddi, S. Kale, F. X. Yu, D. Holtmann-Rice, J. chen and S. Kumar
  Stochastic Negative Mining for Learning with Large Output Spaces
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
  [pdf]
  
  
• Q. Geng, W. Ding, R. Guo, and S. Kumar
  Optimal Noise-Adding Mechanism in Additive Differential Privacy
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.
  [pdf]
  
  
• S. J. Reddi, M. Zaheer, D. Sachan, S. Kale, and S. Kumar
  Adaptive Methods for Nonconvex Optimization
  Neural Information Processing Systems (NIPS), 2018.
  [pdf]
  
  
• N. Agarwal, A. T. Suresh, F. X. Yu, S. Kumar, and H. B. McMahan
  cpSGD: Communication-efficient and differentially-private distributed SGD
  Neural Information Processing Systems (NIPS), 2018.
  [pdf]
  
  
• 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 [best paper award]
  On the Convergence of Adam and Beyond
  International Conference on Learning Representations (ICLR), 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]
  
  
• 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]
  
  
• R. Guo, S. Kumar, K. Choromanski, and D. Simcha
  Quantization based Fast Inner Product Search
  arXiv:1509.01469, 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]
  
  
• 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]
  
  
• 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
  IIEEE 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.
  
  
• Sanjiv 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]
  
  
• S. Kumar and M. Hebert
  A Hierarchical Field Framework for Unified Context-Based Classification
  IEEE International Conference on Computer Vision (ICCV), 2005.
  [pdf]
  
  
• C. Rother, S. Kumar, V. Kolmogorov and A. Blake
  Digital Tapestry
  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]
  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]
  The binary denoising synthetic dataset used for training and testing can be obtained from here.
  
  
• 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]
  
  
• 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]
  
  
• 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]
  
  

Medical Imaging and Robotics

• 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, M. I. Kassim, and K. V. Asari
  Design of a Vision-guided Microrobotic Colonoscopy System
  International Journal of Advanced Robotics, vol. 14, no. 2, pp. 87-104, 2000.
  [link]
  
  
• K. V. Asari, S. Kumar, and M. I. Kassim
  A Fully Autonomous Microrobotic Endoscopy System
  Journal of Intelligent and Robotic Systems, vol. 28, pp. 325-341, 2000.
  [link]
  
  
• K. V. Asari, S. Kumar, and D. Radhakrishnan
  A New Approach for Nonlinear Distortion Correction in Endoscopic Images Based on Least Squares Estimation
  IEEE Transactions on Medical Imaging, vol. 18, no. 4, pp. 345-354,1999.
  [link]
  
  
• S. Kumar, K. V. Asari, and D. Radhakrishnan
  Real-Time Automatic Extraction of Lumen Region and Boundary from Endoscopic Images
  IEE Journal of Medical of Biological Engineering and Computing, vol. 37, pp. 600-604, 1999.
  [link]
  
  
• S. Kumar, K. V. Asari, and D. Radhakrishnan
  Real-Time Automatic Extraction of Lumen Region and Boundary from Endoscopic Images
  IEE Journal of Medical & Biological Engineering and Computing, vol. 37, pp. 600-604, 1999.
  [link]
  
  
• K. V. Asari, S. Kumar, and D. Radhakrishnan
  Technique of Distortion Correction in Endoscopic Images using a Polynomial Expansion
  IEE Journal of Medical & Biological Engineering and Computing, vol. 37, no. 1, pp. 8-12, 1999.
  [link]
  
  
• K. V. Asari, T. Srikanthan, S. Kumar, and D. Radhakrishnan
  A Pipelined Architecture for Image Segmentation by Adaptive Progressive Thresholding
  Journal of Microprocessors and Microsystems, vol. 23, no. 8-9, pp. 493-499, 1999.
  
  
• S. Kumar, K. Vijayan Asari, and D. Radhakrishnan
  Online Extraction of Lumen Region and Boundary from Endoscopic Images Using a Quad Structure
  IEE Conference on Image Processing and its Applications (IPA), pp. 818-822, 1999.
  
  
• S. M. Krishnan, S. Kumar, C. J. Yap, M. I. Kassim, and P. M. Y. Goh
  Development of a Microrobotic System for Intelligent Endoscopy
  2nd Scientific Meet. of Biomed. Eng. Soc., Singapore, January 1999.
  
  
• S. M. Krishnan, S. Kumar, C. J. Yap, M. I. Kassim, and P.M. Y Goh
  A Computer-Based Endoscopic Image Segmentation Technique for Lumen Extraction
  Int. Congress and Exhibition on Comp. Asst. Radio. Surgery, Paris, June, 1999.
  
  
• S. M. Krishnan, S. Kumar, C. J. Yap, M. I. Kassim, and P.M. Y Goh
  A Computer-Based Endoscopic Image Segmentation Technique for Lumen Extraction
  Int. Congress and Exhibition on Comp. Asst. Radio. Surgery, Paris, June, 1999.
  
  
• S. M. Krishnan, S. Kumar, C. J. Yap, M. I. Kassim, and P.M. Y Goh
  Computer-Assisted Intelligent Endoscopy
  Int. Congress and Exhibition on Comp. Asst. Radio. Surgery, Paris, June, 1999.
  
  
• S. Kumar, M. Singaperumal and Y. G. Srinivasa
  Design of a Self Guided Vehicle (SGV) with Laser Based Navigation System
  National Seminar on Mechatronics, India, Madras, 1997.