Kumar, Sanjiv

134 publications

ICLR 2026 Universal Model Routing for Efficient LLM Inference Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Jeevesh Juneja, Congchao Wang, Zifeng Wang, Alec Go, Chen-Yu Lee, Pradeep Shenoy, Rina Panigrahy, Aditya Krishna Menon, Sanjiv Kumar

NeurIPS 2025 Analyzing Similarity Metrics for Data Selection for Language Model Pretraining Dylan Sam, Ayan Chakrabarti, Afshin Rostamizadeh, Srikumar Ramalingam, Gui Citovsky, Sanjiv Kumar

ICLR 2025 Better Autoregressive Regression with LLMs via Regression-Aware Fine-Tuning Michal Lukasik, Zhao Meng, Harikrishna Narasimhan, Yin-Wen Chang, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar

ICML 2025 Bipartite Ranking from Multiple Labels: On Loss Versus Label Aggregation Michal Lukasik, Lin Chen, Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Felix X. Yu, Sashank J. Reddi, Gang Fu, Mohammadhossein Bateni, Sanjiv Kumar

ICLR 2025 Efficient Stagewise Pretraining via Progressive Subnetworks Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

ICLR 2025 Faster Cascades via Speculative Decoding Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar

NeurIPS 2025 Hierarchical Retrieval: The Geometry and a Pretrain-Finetune Recipe Chong You, Rajesh Jayaram, Ananda Theertha Suresh, Robin Nittka, Felix X. Yu, Sanjiv Kumar

ICML 2025 LAuReL: Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar

ICLR 2025 LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization Jui-Nan Yen, Si Si, Zhao Meng, Felix Yu, Sai Surya Duvvuri, Inderjit S Dhillon, Cho-Jui Hsieh, Sanjiv Kumar

ICLRW 2025 Mimetic Initialization Helps State Space Models Learn to Recall Asher Trockman, Hrayr Harutyunyan, J Zico Kolter, Sanjiv Kumar, Srinadh Bhojanapalli

ICLR 2025 Reasoning with Latent Thoughts: On the Power of Looped Transformers Nikunj Saunshi, Nishanth Dikkala, Zhiyuan Li, Sanjiv Kumar, Sashank J. Reddi

NeurIPS 2025 Scalable In-Context Ranking with Generative Models Nilesh Gupta, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Inderjit S Dhillon, Felix X. Yu

ICLRW 2025 SoftSRV: Learn to Generate Targeted Synthetic Data. Giulia DeSalvo, Jean-François Kagy, Lazaros Karydas, Afshin Rostamizadeh, Sanjiv Kumar

NeurIPS 2025 Spark Transformer: Reactivating Sparsity in Transformer FFN and Attention Chong You, Kan Wu, Zhipeng Jia, Lin Chen, Srinadh Bhojanapalli, Jiaxian Guo, Utku Evci, Jan Wassenberg, Praneeth Netrapalli, Jeremiah J. Willcock, Suvinay Subramanian, Felix Chern, Alek Andreev, Shreya Pathak, Felix X. Yu, Prateek Jain, David E Culler, Henry Levy, Sanjiv Kumar

ICML 2025 Structured Preconditioners in Adaptive Optimization: A Unified Analysis Shuo Xie, Tianhao Wang, Sashank J. Reddi, Sanjiv Kumar, Zhiyuan Li

ICLRW 2025 Universal LLM Routing with Correctness-Based Representation Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Jeevesh Juneja, Zifeng Wang, Chen-Yu Lee, Pradeep Shenoy, Rina Panigrahy, Aditya Krishna Menon, Sanjiv Kumar

NeurIPS 2024 Accelerating Blockwise Parallel Language Models with Draft Refinement Taehyeon Kim, Ananda Theertha Suresh, Kishore Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton

ICML 2024 Can Looped Transformers Learn to Implement Multi-Step Gradient Descent for In-Context Learning? Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar

ICLR 2024 DistillSpec: Improving Speculative Decoding via Knowledge Distillation Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal

ICMLW 2024 Exploring and Improving Drafts in Blockwise Parallel Decoding Taehyeon Kim, Ananda Theertha Suresh, Kishore A Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton

ICLR 2024 Functional Interpolation for Relative Positions Improves Long Context Transformers Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli

ICMLW 2024 LAuReL: Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar

ICLR 2024 Language Model Cascades: Token-Level Uncertainty and Beyond Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

ICLR 2024 Learning to Reject Meets Long-Tail Learning Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Neha Gupta, Sanjiv Kumar

CVPR 2024 MarkovGen: Structured Prediction for Efficient Text-to-Image Generation Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar

ICLR 2024 On Bias-Variance Alignment in Deep Models Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar

NeurIPS 2024 On the Inductive Bias of Stacking Towards Improving Reasoning Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank J. Reddi, Sanjiv Kumar

ICLR 2024 Plugin Estimators for Selective Classification with Out-of-Distribution Detection Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar

ICML 2024 Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore Papineni, Sanjiv Kumar, Andrej Risteski

ICLRW 2024 Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines Yuchen Li, Alexandre Kirchmeyer, Aashay Mehta, Yilong Qin, Boris Dadachev, Kishore A Papineni, Sanjiv Kumar, Andrej Risteski

ICLRW 2024 REST Meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent Renat Aksitov, Sobhan Miryoosefi, Zonglin Li, Daliang Li, Sheila Babayan, Kavya Kopparapu, Zachary Fisher, Ruiqi Guo, Sushant Prakash, Pranesh Srinivasan, Manzil Zaheer, Felix Yu, Sanjiv Kumar

CVPR 2024 Rethinking FID: Towards a Better Evaluation Metric for Image Generation Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar

ICML 2024 Tandem Transformers for Inference Efficient LLMs P S Aishwarya, Pranav Ajit Nair, B L Yashas Samaga, Toby James Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli

ICLR 2024 Think Before You Speak: Training Language Models with Pause Tokens Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan

ICLR 2024 Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar

ICML 2024 USTAD: Unified Single-Model Training Achieving Diverse Scores for Information Retrieval Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Wittawat Jitkrittum, Veeranjaneyulu Sadhanala, Sadeep Jayasumana, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar

TMLR 2024 What Do Larger Image Classifiers Memorise? Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

ICLR 2023 Automating Nearest Neighbor Search Configuration with Constrained Optimization Philip Sun, Ruiqi Guo, Sanjiv Kumar

ICML 2023 Efficient Training of Language Models Using Few-Shot Learning Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar

ICLR 2023 Leveraging Importance Weights in Subset Selection Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang

NeurIPS 2023 On Student-Teacher Deviations in Distillation: Does It Pay to Disobey? Vaishnavh Nagarajan, Aditya K Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar

NeurIPS 2023 ResMem: Learn What You Can and Memorize the REST Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Rawat, Manzil Zaheer, Aditya K Menon, Sanjiv Kumar

NeurIPS 2023 SOAR: Improved Indexing for Approximate Nearest Neighbor Search Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar

ICLR 2023 Serving Graph Compression for Graph Neural Networks Si Si, Felix Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar

ICMLW 2023 SpecTr: Fast Speculative Decoding via Optimal Transport Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix Yu, Michael Riley, Sanjiv Kumar

ICLR 2023 Supervision Complexity and Its Role in Knowledge Distillation Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar

ICLR 2023 Teacher Guided Training: An Efficient Framework for Knowledge Transfer Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar

ICLR 2023 The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix Chern, Felix Yu, Ruiqi Guo, Sanjiv Kumar

NeurIPSW 2023 Think Before You Speak: Training Language Models with Pause Tokens Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan

NeurIPSW 2023 Two-Stage LLM Fine-Tuning with Less Specialization and More Generalization Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar

NeurIPS 2023 When Does Confidence-Based Cascade Deferral Suffice? Wittawat Jitkrittum, Neha Gupta, Aditya K Menon, Harikrishna Narasimhan, Ankit Rawat, Sanjiv Kumar

NeurIPS 2022 Decoupled Context Processing for Context Augmented Language Modeling Zonglin Li, Ruiqi Guo, Sanjiv Kumar

ICML 2022 In Defense of Dual-Encoders for Neural Ranking Aditya Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar

NeurIPS 2022 Post-Hoc Estimators for Learning to Defer to an Expert Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya K Menon, Ankit Rawat, Sanjiv Kumar

ICML 2022 Robust Training of Neural Networks Using Scale Invariant Architectures Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank Reddi, Sanjiv Kumar

NeurIPS 2022 TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar

TMLR 2022 Teacher’s Pet: Understanding and Mitigating Biases in Distillation Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar

NeurIPSW 2022 When Does Mixup Promote Local Linearity in Learned Representations? Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit, Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar

AISTATS 2021 RankDistil: Knowledge Distillation for Ranking Sashank Reddi, Rama Kumar Pasumarthi, Aditya Menon, Ankit Singh Rawat, Felix Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar

ICML 2021 A Statistical Perspective on Distillation Aditya K Menon, Ankit Singh Rawat, Sashank Reddi, Seungyeon Kim, Sanjiv Kumar

ICLR 2021 Adaptive Federated Optimization Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, Hugh Brendan McMahan

NeurIPS 2021 Batch Active Learning at Scale Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar

ICLR 2021 Coping with Label Shift via Distributionally Robust Optimisation Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra

ICML 2021 Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces Ankit Singh Rawat, Aditya K Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix Yu, Sashank Reddi, Sanjiv Kumar

NeurIPS 2021 Efficient Training of Retrieval Models Using Negative Cache Erik Lindgren, Sashank Reddi, Ruiqi Guo, Sanjiv Kumar

ICLR 2021 Evaluations and Methods for Explanation Through Robustness Analysis Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh

ICLR 2021 Long-Tail Learning via Logit Adjustment Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar

ICLR 2021 Overparameterisation and Worst-Case Generalisation: Friend or Foe? Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

ICML 2020 Accelerating Large-Scale Inference with Anisotropic Vector Quantization Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar

ICLR 2020 Are Transformers Universal Approximators of Sequence-to-Sequence Functions? Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar

ICLR 2020 Can Gradient Clipping Mitigate Label Noise? Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar

ICML 2020 Does Label Smoothing Mitigate Label Noise? Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, Sanjiv Kumar

ICML 2020 Federated Learning with Only Positive Labels Felix Yu, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar

CVPR 2020 How Does Noise Help Robustness? Explanation and Exploration Under the Neural SDE Framework Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh

ICLR 2020 Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh

NeurIPS 2020 Learning Discrete Distributions: User vs Item-Level Privacy Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Michael Riley

ICLR 2020 Learning to Learn by Zeroth-Order Oracle Yangjun Ruan, Yuanhao Xiong, Sashank Reddi, Sanjiv Kumar, Cho-Jui Hsieh

ICML 2020 Low-Rank Bottleneck in Multi-Head Attention Models Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

NeurIPS 2020 Multi-Stage Influence Function Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, Cho-Jui Hsieh

ICLR 2020 New Loss Functions for Fast Maximum Inner Product Search Ruiqi Guo, Quan Geng, David Simcha, Felix Chern, Phil Sun, Sanjiv Kumar

NeurIPS 2020 O(n) Connections Are Expressive Enough: Universal Approximability of Sparse Transformers Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

ICLR 2020 Pre-Training Tasks for Embedding-Based Large-Scale Retrieval Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar

NeurIPS 2020 Robust Large-Margin Learning in Hyperbolic Space Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya K Menon, Sanjiv Kumar

AISTATS 2020 Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar

NeurIPS 2020 Why Are Adaptive Methods Good for Attention Models? Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, Suvrit Sra

NeurIPS 2019 Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces Chuan Guo, Ali Mousavi, Xiang Wu, Daniel N Holtmann-Rice, Satyen Kale, Sashank Reddi, Sanjiv Kumar

ICML 2019 Escaping Saddle Points with Adaptive Gradient Methods Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra

AAAI 2019 Learning Adaptive Random Features Yanjun Li, Kai Zhang, Jun Wang, Sanjiv Kumar

ICML 2019 Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

ICLR 2019 Learning to Screen for Fast SoftMax Inference on Large Vocabulary Neural Networks Patrick Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh

NeurIPS 2019 Multilabel Reductions: What Is My Loss Optimising? Aditya K Menon, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

AISTATS 2019 Optimal Noise-Adding Mechanism in Additive Differential Privacy Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar

NeurIPS 2019 Sampled SoftMax with Random Fourier Features Ankit Singh Rawat, Jiecao Chen, Felix Xinnan X Yu, Ananda Theertha Suresh, Sanjiv Kumar

AISTATS 2019 Stochastic Negative Mining for Learning with Large Output Spaces Sashank J. Reddi, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Jiecao Chen, Sanjiv Kumar

NeurIPS 2018 Adaptive Methods for Nonconvex Optimization Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar

ICML 2018 Loss Decomposition for Fast Learning in Large Output Spaces Ian En-Hsu Yen, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar

ICLR 2018 On the Convergence of Adam and Beyond Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

NeurIPS 2018 cpSGD: Communication-Efficient and Differentially-Private Distributed SGD Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Brendan McMahan

ICML 2017 Distributed Mean Estimation with Limited Communication Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan

AISTATS 2017 Fast Classification with Binary Prototypes Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon

ICCV 2017 Learning Spread-Out Local Feature Descriptors Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang

NeurIPS 2017 Multiscale Quantization for Fast Similarity Search Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N Holtmann-Rice, David Simcha, Felix Yu

ICML 2017 Stochastic Generative Hashing Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song

ICML 2016 Binary Embeddings with Structured Hashed Projections Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun

NeurIPS 2016 Orthogonal Random Features Felix Xinnan X Yu, Ananda Theertha Suresh, Krzysztof M Choromanski, Daniel N Holtmann-Rice, Sanjiv Kumar

AISTATS 2016 Quantization Based Fast Inner Product Search Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha

ICCV 2015 An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections Yu Cheng, Felix X. Yu, Rogerio S. Feris, Sanjiv Kumar, Alok Choudhary, Shi-Fu Chang

ICCV 2015 Fast Orthogonal Projection Based on Kronecker Product Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, Shi-Fu Chang

NeurIPS 2015 Spherical Random Features for Polynomial Kernels Jeffrey Pennington, Felix Xinnan X Yu, Sanjiv Kumar

NeurIPS 2015 Structured Transforms for Small-Footprint Deep Learning Vikas Sindhwani, Tara Sainath, Sanjiv Kumar

ICML 2014 Circulant Binary Embedding Felix Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang

NeurIPS 2014 Discrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang

JMLR 2013 Large-Scale SVD and Manifold Learning Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry Rowley

CVPR 2013 Learning Binary Codes for High-Dimensional Data Using Bilinear Projections Yunchao Gong, Sanjiv Kumar, Henry A. Rowley, Svetlana Lazebnik

ICML 2013 \proptoSVM for Learning with Label Proportions Felix Yu, Dong Liu, Sanjiv Kumar, Jebara Tony, Shih-Fu Chang

NeurIPS 2012 Angular Quantization-Based Binary Codes for Fast Similarity Search Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik

ICML 2012 Compact Hyperplane Hashing with Bilinear Functions Wei Liu, Jun Wang, Yadong Mu, Sanjiv Kumar, Shih-Fu Chang

ICML 2012 On the Difficulty of Nearest Neighbor Search Junfeng He, Sanjiv Kumar, Shih-Fu Chang

JMLR 2012 Sampling Methods for the Nystr M Method Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

ICML 2011 Hashing with Graphs Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang

ICML 2010 Sequential Projection Learning for Hashing with Compact Codes Jun Wang, Sanjiv Kumar, Shih-Fu Chang

CVPR 2010 YouTubeCat: Learning to Categorize Wild Web Videos Zheshen Wang, Ming Zhao, Yang Song, Sanjiv Kumar, Baoxin Li

NeurIPS 2009 Ensemble Nystrom Method Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

ICML 2009 On Sampling-Based Approximate Spectral Decomposition Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

AISTATS 2009 Sampling Techniques for the Nystrom Method Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

ECCV 2008 A New Baseline for Image Annotation Ameesh Makadia, Vladimir Pavlovic, Sanjiv Kumar

CVPR 2008 Face Tracking and Recognition with Visual Constraints in Real-World Videos Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic, Henry A. Rowley

CVPR 2008 Large-Scale Manifold Learning Ameet Talwalkar, Sanjiv Kumar, Henry A. Rowley

ICCV 2007 Classification of Weakly-Labeled Data with Partial Equivalence Relations Sanjiv Kumar, Henry A. Rowley

ICCV 2005 A Hierarchical Field Framework for Unified Context-Based Classification Sanjiv Kumar, Martial Hebert

CVPR 2005 Digital Tapestry Carsten Rother, Sanjiv Kumar, Vladimir Kolmogorov, Andrew Blake

NeurIPS 2003 Discriminative Fields for Modeling Spatial Dependencies in Natural Images Sanjiv Kumar, Martial Hebert

ICCV 2003 Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification Sanjiv Kumar, Martial Hebert

CVPR 2003 Man-Made Structure Detection in Natural Images Using a Causal Multiscale Random Field Sanjiv Kumar, Martial Hebert