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2020 – today
- 2020
[c67]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy. AISTATS 2020: 89-99- [c66]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework. CVPR 2020: 279-287 - [c65]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. EMNLP (1) 2020: 4992-4998 - [c64]Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar:
Pre-training Tasks for Embedding-based Large-scale Retrieval. ICLR 2020 - [c63]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Can gradient clipping mitigate label noise? ICLR 2020 - [c62]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. ICLR 2020 - [c61]Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh:
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. ICLR 2020 - [c60]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? ICLR 2020 - [c59]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. ICML 2020: 864-873 - [c58]Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar:
Accelerating Large-Scale Inference with Anisotropic Vector Quantization. ICML 2020: 3887-3896 - [c57]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? ICML 2020: 6448-6458 - [c56]Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Federated Learning with Only Positive Labels. ICML 2020: 10946-10956 - [c55]Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Robust large-margin learning in hyperbolic space. NeurIPS 2020 - [c54]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. NeurIPS 2020 - [c53]Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley:
Learning discrete distributions: user vs item-level privacy. NeurIPS 2020 - [c52]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers. NeurIPS 2020 - [c51]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why are Adaptive Methods Good for Attention Models? NeurIPS 2020 - [i54]Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar:
Pre-training Tasks for Embedding-based Large-scale Retrieval. CoRR abs/2002.03932 (2020) - [i53]Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Low-Rank Bottleneck in Multi-head Attention Models. CoRR abs/2002.07028 (2020) - [i52]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, H. Brendan McMahan:
Adaptive Federated Optimization. CoRR abs/2003.00295 (2020) - [i51]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? CoRR abs/2003.02819 (2020) - [i50]Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Robust Large-Margin Learning in Hyperbolic Space. CoRR abs/2004.05465 (2020) - [i49]Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Federated Learning with Only Positive Labels. CoRR abs/2004.10342 (2020) - [i48]Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Doubly-stochastic mining for heterogeneous retrieval. CoRR abs/2004.10915 (2020) - [i47]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
Why distillation helps: a statistical perspective. CoRR abs/2005.10419 (2020) - [i46]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. CoRR abs/2006.00442 (2020) - [i45]Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers. CoRR abs/2006.04862 (2020) - [i44]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. CoRR abs/2007.07314 (2020) - [i43]Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh:
Multi-Stage Influence Function. CoRR abs/2007.09081 (2020) - [i42]Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley:
Learning discrete distributions: user vs item-level privacy. CoRR abs/2007.13660 (2020) - [i41]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. CoRR abs/2010.07447 (2020) - [i40]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. CoRR abs/2010.12230 (2020) - [i39]Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix X. Yu, Sanjiv Kumar:
Modifying Memories in Transformer Models. CoRR abs/2012.00363 (2020) - [i38]Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar:
Kernelized Classification in Deep Networks. CoRR abs/2012.09607 (2020)
2010 – 2019
- 2019
- [c50]Yanjun Li, Kai Zhang, Jun Wang, Sanjiv Kumar:
Learning Adaptive Random Features. AAAI 2019: 4229-4236 - [c49]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Optimal Noise-Adding Mechanism in Additive Differential Privacy. AISTATS 2019: 11-20 - [c48]Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Jiecao Chen, Sanjiv Kumar:
Stochastic Negative Mining for Learning with Large Output Spaces. AISTATS 2019: 1940-1949 - [c47]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. ICLR (Poster) 2019 - [c46]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. ICML 2019: 5956-5965 - [c45]Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling. ICML 2019: 6828-6839 - [c44]Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar:
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. NeurIPS 2019: 4944-4954 - [c43]Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar:
Sampled Softmax with Random Fourier Features. NeurIPS 2019: 13834-13844 - [i37]Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. CoRR abs/1901.09149 (2019) - [i36]Xiang Wu, Ruiqi Guo, David Simcha, Dave Dopson, Sanjiv Kumar:
Efficient Inner Product Approximation in Hybrid Spaces. CoRR abs/1903.08690 (2019) - [i35]Xiang Wu, Ruiqi Guo, Sanjiv Kumar, David Simcha:
Local Orthogonal Decomposition for Maximum Inner Product Search. CoRR abs/1903.10391 (2019) - [i34]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. CoRR abs/1904.09237 (2019) - [i33]Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh:
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise. CoRR abs/1906.02355 (2019) - [i32]Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar:
Sampled Softmax with Random Fourier Features. CoRR abs/1907.10747 (2019) - [i31]Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
AdaCliP: Adaptive Clipping for Private SGD. CoRR abs/1908.07643 (2019) - [i30]Ruiqi Guo, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar, Xiang Wu:
New Loss Functions for Fast Maximum Inner Product Search. CoRR abs/1908.10396 (2019) - [i29]Aditya Krishna Menon, Anand Rajagopalan, Baris Sumengen, Gui Citovsky, Qin Cao, Sanjiv Kumar:
Online Hierarchical Clustering Approximations. CoRR abs/1909.09667 (2019) - [i28]Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh:
Learning to Learn by Zeroth-Order Oracle. CoRR abs/1910.09464 (2019) - [i27]Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra:
Why ADAM Beats SGD for Attention Models. CoRR abs/1912.03194 (2019) - [i26]Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Are Transformers universal approximators of sequence-to-sequence functions? CoRR abs/1912.10077 (2019) - 2018
- [c42]Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
On the Convergence of Adam and Beyond. ICLR 2018 - [c41]Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar:
Loss Decomposition for Fast Learning in Large Output Spaces. ICML 2018: 5626-5635 - [c40]Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan:
cpSGD: Communication-efficient and differentially-private distributed SGD. NeurIPS 2018: 7575-7586 - [c39]Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar:
Adaptive Methods for Nonconvex Optimization. NeurIPS 2018: 9815-9825 - [i25]Si Si, Sanjiv Kumar, Yang Li:
Nonlinear Online Learning with Adaptive Nyström Approximation. CoRR abs/1802.07887 (2018) - [i24]Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan:
cpSGD: Communication-efficient and differentially-private distributed SGD. CoRR abs/1805.10559 (2018) - [i23]Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
The Sparse Recovery Autoencoder. CoRR abs/1806.10175 (2018) - [i22]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Optimal Noise-Adding Mechanism in Additive Differential Privacy. CoRR abs/1809.10224 (2018) - [i21]Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar:
Truncated Laplacian Mechanism for Approximate Differential Privacy. CoRR abs/1810.00877 (2018) - [i20]Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel N. Holtmann-Rice, Jiecao Chen, Sanjiv Kumar:
Stochastic Negative Mining for Learning with Large Output Spaces. CoRR abs/1810.07076 (2018) - [i19]Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh:
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. CoRR abs/1810.12406 (2018) - 2017
- [j14]Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang:
On Binary Embedding using Circulant Matrices. J. Mach. Learn. Res. 18: 150:1-150:30 (2017) - [c38]Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon:
Fast Classification with Binary Prototypes. AISTATS 2017: 1255-1263 - [c37]Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang:
Learning Spread-Out Local Feature Descriptors. ICCV 2017: 4605-4613 - [c36]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. ICML 2017: 913-922 - [c35]Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan:
Distributed Mean Estimation with Limited Communication. ICML 2017: 3329-3337 - [c34]Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N. Holtmann-Rice, David Simcha, Felix X. Yu:
Multiscale Quantization for Fast Similarity Search. NIPS 2017: 5745-5755 - [i18]Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song:
Stochastic Generative Hashing. CoRR abs/1701.02815 (2017) - [i17]Matthew L. Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, László Lukács, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, Ray Kurzweil:
Efficient Natural Language Response Suggestion for Smart Reply. CoRR abs/1705.00652 (2017) - [i16]Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang:
Learning Spread-out Local Feature Descriptors. CoRR abs/1708.06320 (2017) - [i15]Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo, Kevin Kilgour, Sanjiv Kumar, James Lyon, Julian Odell, Marvin Ritter, Dominik Roblek, Matthew Sharifi, Mihajlo Velimirovic:
Now Playing: Continuous low-power music recognition. CoRR abs/1711.10958 (2017) - 2016
- [j13]Sanjiv Kumar, Ritika Chopra, Ratnesh Rajan Saxena
:
A Fast Approach to Solve Matrix Games with Payoffs of Trapezoidal Fuzzy Numbers. Asia Pac. J. Oper. Res. 33(6): 1-14 (2016) - [j12]Wonjun Lee, Sanjiv Kumar:
Software-Defined Storage-Based Data Infrastructure Supportive of Hydroclimatology Simulation Containers: A Survey. Data Sci. Eng. 1(2): 65-72 (2016) - [j11]Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang:
Learning to Hash for Indexing Big Data - A Survey. Proc. IEEE 104(1): 34-57 (2016) - [c33]Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha:
Quantization based Fast Inner Product Search. AISTATS 2016: 482-490 - [c32]Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun:
Binary embeddings with structured hashed projections. ICML 2016: 344-353 - [c31]Felix X. Yu, Ananda Theertha Suresh, Krzysztof Marcin Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar:
Orthogonal Random Features. NIPS 2016: 1975-1983 - [i14]Felix X. Yu, Ananda Theertha Suresh, Krzysztof Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar:
Orthogonal Random Features. CoRR abs/1610.09072 (2016) - [i13]Ananda Theertha Suresh, Felix X. Yu, H. Brendan McMahan, Sanjiv Kumar:
Distributed Mean Estimation with Limited Communication. CoRR abs/1611.00429 (2016) - 2015
- [c30]Yanbo Xu, Olivier Siohan, David Simcha, Sanjiv Kumar, Hank Liao:
Exemplar-based large vocabulary speech recognition using k-nearest neighbors. ICASSP 2015: 5167-5171 - [c29]Yu Cheng, Felix X. Yu, Rogério Schmidt Feris, Sanjiv Kumar, Alok N. Choudhary, Shih-Fu Chang:
An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections. ICCV 2015: 2857-2865 - [c28]Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, Shih-Fu Chang:
Fast Orthogonal Projection Based on Kronecker Product. ICCV 2015: 2929-2937 - [c27]Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
A Survey of Modern Questions and Challenges in Feature Extraction. FE@NIPS 2015: 1-18 - [c26]Jeffrey Pennington, Felix X. Yu, Sanjiv Kumar:
Spherical Random Features for Polynomial Kernels. NIPS 2015: 1846-1854 - [c25]Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar:
Structured Transforms for Small-Footprint Deep Learning. NIPS 2015: 3088-3096 - [p2]Felix X. Yu, Yunchao Gong, Sanjiv Kumar:
Fast Binary Embedding for High-Dimensional Data. Multimedia Data Mining and Analytics 2015: 347-371 - [i12]Yu Cheng, Felix X. Yu, Rogério Schmidt Feris, Sanjiv Kumar, Alok N. Choudhary, Shih-Fu Chang:
Fast Neural Networks with Circulant Projections. CoRR abs/1502.03436 (2015) - [i11]Felix X. Yu, Sanjiv Kumar, Henry A. Rowley, Shih-Fu Chang:
Compact Nonlinear Maps and Circulant Extensions. CoRR abs/1503.03893 (2015) - [i10]Krzysztof Choromanski, Sanjiv Kumar, Xiaofeng Liu:
Fast Online Clustering with Randomized Skeleton Sets. CoRR abs/1506.03425 (2015) - [i9]Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha:
Quantization based Fast Inner Product Search. CoRR abs/1509.01469 (2015) - [i8]Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang:
Learning to Hash for Indexing Big Data - A Survey. CoRR abs/1509.05472 (2015) - [i7]Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar:
Structured Transforms for Small-Footprint Deep Learning. CoRR abs/1510.01722 (2015) - [i6]Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun:
Binary embeddings with structured hashed projections. CoRR abs/1511.05212 (2015) - [i5]Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang:
On Binary Embedding using Circulant Matrices. CoRR abs/1511.06480 (2015) - 2014
- [c24]Felix X. Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang:
Circulant Binary Embedding. ICML 2014: 946-954 - [c23]Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang:
Discrete Graph Hashing. NIPS 2014: 3419-3427 - [r1]Sanjiv Kumar:
Discriminative Random Fields. Computer Vision, A Reference Guide 2014: 221-229 - [i4]Felix X. Yu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang:
On Learning with Label Proportions. CoRR abs/1402.5902 (2014) - [i3]Felix X. Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang:
Circulant Binary Embedding. CoRR abs/1405.3162 (2014) - 2013
- [j10]Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry A. Rowley:
Large-scale SVD and manifold learning. J. Mach. Learn. Res. 14(1): 3129-3152 (2013) - [c22]Yunchao Gong, Sanjiv Kumar, Henry A. Rowley, Svetlana Lazebnik:
Learning Binary Codes for High-Dimensional Data Using Bilinear Projections. CVPR 2013: 484-491 - [c21]Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang:
\(\propto\)SVM for Learning with Label Proportions. ICML (3) 2013: 504-512 - [i2]Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang:
$\propto$SVM for learning with label proportions. CoRR abs/1306.0886 (2013) - 2012
- [j9]Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
Sampling Methods for the Nyström Method. J. Mach. Learn. Res. 13: 981-1006 (2012) - [j8]Jun Wang, Sanjiv Kumar, Shih-Fu Chang:
Semi-Supervised Hashing for Large-Scale Search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12): 2393-2406 (2012) - [c20]Junfeng He, Sanjiv Kumar, Shih-Fu Chang:
On the Difficulty of Nearest Neighbor Search. ICML 2012 - [c19]Wei Liu, Jun Wang, Yadong Mu, Sanjiv Kumar, Shih-Fu Chang:
Compact Hyperplane Hashing with Bilinear Functions. ICML 2012 - [c18]Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik:
Angular Quantization-based Binary Codes for Fast Similarity Search. NIPS 2012: 1205-1213 - [i1]Wei Liu, Jun Wang, Yadong Mu, Sanjiv Kumar, Shih-Fu Chang:
Compact Hyperplane Hashing with Bilinear Functions. CoRR abs/1206.4618 (2012) - 2011
- [c17]Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang:
Hashing with Graphs. ICML 2011: 1-8 - 2010
- [j7]Ameesh Makadia, Vladimir Pavlovic, Sanjiv Kumar:
Baselines for Image Annotation. Int. J. Comput. Vis. 90(1): 88-105 (2010) - [c16]Zheshen Wang, Ming Zhao, Yang Song, Sanjiv Kumar, Baoxin Li:
YouTubeCat: Learning to categorize wild web videos. CVPR 2010: 879-886 - [c15]Jun Wang, Sanjiv Kumar, Shih-Fu Chang:
Sequential Projection Learning for Hashing with Compact Codes. ICML 2010: 1127-1134 - [p1]Sanjiv Kumar:
Discriminative Graphical Models for Context-Based Classification. Computer Vision: Detection, Recognition and Reconstruction 2010: 109-134
2000 – 2009
- 2009
- [c14]Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
On sampling-based approximate spectral decomposition. ICML 2009: 553-560 - [c13]Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
Ensemble Nystrom Method. NIPS 2009: 1060-1068 - [c12]Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar:
Sampling Techniques for the Nystrom Method. AISTATS 2009: 304-311 - 2008
- [c11]Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic, Henry A. Rowley:
Face tracking and recognition with visual constraints in real-world videos. CVPR 2008 - [c10]Ameet Talwalkar, Sanjiv Kumar, Henry A. Rowley:
Large-scale manifold learning. CVPR 2008 - [c9]Ameesh Makadia, Vladimir Pavlovic, Sanjiv Kumar:
A New Baseline for Image Annotation. ECCV (3) 2008: 316-329 - 2007
- [c8]Sanjiv Kumar, Henry A. Rowley:
Classification of Weakly-Labeled Data with Partial Equivalence Relations. ICCV 2007: 1-8 - 2006
- [j6]Sanjiv Kumar, Martial Hebert:
Discriminative Random Fields. Int. J. Comput. Vis. 68(2): 179-201 (2006) - 2005
- [c7]Carsten Rother, Sanjiv Kumar, Vladimir Kolmogorov, Andrew Blake:
Digital Tapestry. CVPR (1) 2005: 589-596 - [c6]Sanjiv Kumar, Jonas August, Martial Hebert:
Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study. EMMCVPR 2005: 153-168 - [c5]Sanjiv Kumar, Martial Hebert:
A Hierarchical Field Framework for Unified Context-Based Classification. ICCV 2005: 1284-1291 - 2004
- [c4]Bart C. Nabbe, Sanjiv Kumar, Martial Hebert:
Path planning with hallucinated worlds. IROS 2004: 3123-3130 - 2003
- [j5]Sanjiv Kumar, Alexander C. Loui, Martial Hebert:
An observation-constrained generative approach for probabilistic classification of image regions. Image Vis. Comput. 21(1): 87-97 (2003) - [c3]Sanjiv Kumar, Martial Hebert:
Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field. CVPR (1) 2003: 119-126 - [c2]Sanjiv Kumar, Martial Hebert:
Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification. ICCV 2003: 1150-1159 - [c1]