Sanjiv Kumar

According to our database, Sanjiv Kumar authored at least 165 papers between 1999 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Metric-aware LLM inference.

Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix X. Yu, Sanjiv Kumar

CoRR, 2024

HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference.

Yashas Samaga, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli

CoRR, 2024

Tandem Transformers for Inference Efficient LLMs.

Aishwarya P. S, Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli

CoRR, 2024

Efficient Stagewise Pretraining via Progressive Subnetworks.

Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

CoRR, 2024

SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection.

Ke Ye, Heinrich Jiang, Afshin Rostamizadeh, Ayan Chakrabarti, Giulia DeSalvo, Jean-François Kagy, Lazaros Karydas, Gui Citovsky, Sanjiv Kumar

CoRR, 2024

Rethinking FID: Towards a Better Evaluation Metric for Image Generation.

Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar

CoRR, 2024

2023

A Weighted K-Center Algorithm for Data Subset Selection.

Srikumar Ramalingam, Pranjal Awasthi, Sanjiv Kumar

CoRR, 2023

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 X. Yu, Sanjiv Kumar

CoRR, 2023

It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models.

Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar

CoRR, 2023

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

CoRR, 2023

What do larger image classifiers memorise?

Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

CoRR, 2023

Functional Interpolation for Relative Positions Improves Long Context Transformers.

Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontañón, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli

CoRR, 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

CoRR, 2023

SPEGTI: Structured Prediction for Efficient Generative Text-to-Image Models.

Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar

CoRR, 2023

Depth Dependence of μP Learning Rates in ReLU MLPs.

Samy Jelassi, Boris Hanin, Ziwei Ji, Sashank J. Reddi, Srinadh Bhojanapalli, Sanjiv Kumar

CoRR, 2023

Learning to reject meets OOD detection: Are all abstentions created equal?

Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar

CoRR, 2023

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval.

Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Sadeep Jayasumana, Veeranjaneyulu Sadhanala, Wittawat Jitkrittum, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar

CoRR, 2023

ResMem: Learn what you can and memorize the rest.

Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SOAR: Improved Indexing for Approximate Nearest Neighbor Search.

Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On student-teacher deviations in distillation: does it pay to disobey?

Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When Does Confidence-Based Cascade Deferral Suffice?

Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Framework for Developing the Next Generation Interactive Soil Moisture Forecasting System Using the Long-Short Term Memory Model.

Guna Shekar M., Wonjun Lee, Sanjiv Kumar, Yanan Duan, Imtiaz Rangwala

Proceedings of the International Conference on Machine Learning and Applications, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Automating Nearest Neighbor Search Configuration with Constrained Optimization.

Philip Sun, Ruiqi Guo, Sanjiv Kumar

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Serving Graph Compression for Graph Neural Networks.

Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar

Proceedings of the Eleventh International Conference on Learning Representations, 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 X. Yu, Ruiqi Guo, Sanjiv Kumar

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Supervision Complexity and its Role in Knowledge Distillation.

Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Leveraging Importance Weights in Subset Selection.

Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Large Language Models with Controllable Working Memory.

Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, Sanjiv Kumar

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022

Teacher's pet: understanding and mitigating biases in distillation.

Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar

Trans. Mach. Learn. Res., 2022

Large Language Models with Controllable Working Memory.

Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, Sanjiv Kumar

CoRR, 2022

Preserving In-Context Learning ability in Large Language Model Fine-tuning.

Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar

CoRR, 2022

When does mixup promote local linearity in learned representations?

Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit, Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar

CoRR, 2022

Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers.

Zonglin Li, Chong You, Srinadh Bhojanapalli, Daliang Li, Ankit Singh Rawat, Sashank J. Reddi, Ke Ye, Felix Chern, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar

CoRR, 2022

ELM: Embedding and Logit Margins for Long-Tail Learning.

Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

CoRR, 2022

Predicting on the Edge: Identifying Where a Larger Model Does Better.

Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar

CoRR, 2022

Post-hoc estimators for learning to defer to an expert.

Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Decoupled Context Processing for Context Augmented Language Modeling.

Zonglin Li, Ruiqi Guo, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s.

Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

In defense of dual-encoders for neural ranking.

Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the International Conference on Machine Learning, 2022

Robust Training of Neural Networks Using Scale Invariant Architectures.

Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the International Conference on Machine Learning, 2022

Radio over FSO System for 5G Wireless Communication.

Gautam Gautam, Neeraj Sharma, Sanjiv Kumar

Proceedings of the 13th International Conference on Computing Communication and Networking Technologies, 2022

2021

Routing Protocols in Delay Tolerant Networks: Comparative and Empirical Analysis.

Ankita Verma, Savita, Sanjiv Kumar

Wirel. Pers. Commun., 2021

When in Doubt, Summon the Titans: Efficient Inference with Large Models.

Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar

CoRR, 2021

Leveraging redundancy in attention with Reuse Transformers.

Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar

CoRR, 2021

Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation.

Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain, Sanjiv Kumar, Michal Lukasik, Andreas Veit

CoRR, 2021

Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets.

Baris Sumengen, Anand Rajagopalan, Gui Citovsky, David Simcha, Olivier Bachem, Pradipta Mitra, Sam Blasiak, Mason Liang, Sanjiv Kumar

CoRR, 2021

Balancing Robustness and Sensitivity using Feature Contrastive Learning.

Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam, Cho-Jui Hsieh, Kishore Papineni, Sanjiv Kumar

CoRR, 2021

Balancing Constraints and Submodularity in Data Subset Selection.

Srikumar Ramalingam, Daniel Glasner, Kaushal Patel, Raviteja Vemulapalli, Sadeep Jayasumana, Sanjiv Kumar

CoRR, 2021

On the Reproducibility of Neural Network Predictions.

Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Krishna Menon, Sanjiv Kumar

CoRR, 2021

Efficient Training of Retrieval Models using Negative Cache.

Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Batch Active Learning at Scale.

Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces.

Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the 38th International Conference on Machine Learning, 2021

A statistical perspective on distillation.

Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar

Proceedings of the 38th International Conference on Machine Learning, 2021

Coping with Label Shift via Distributionally Robust Optimisation.

Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra

Proceedings of the 9th International Conference on Learning Representations, 2021

Adaptive Federated Optimization.

Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan

Proceedings of the 9th International Conference on Learning Representations, 2021

Overparameterisation and worst-case generalisation: friend or foe?

Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar

Proceedings of the 9th International Conference on Learning Representations, 2021

Long-tail learning via logit adjustment.

Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the 9th International Conference on Learning Representations, 2021

RankDistil: Knowledge Distillation for Ranking.

Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Kernelized Classification in Deep Networks.

Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar

CoRR, 2020

Modifying Memories in Transformer Models.

Chen Zhu, Ankit Singh Rawat, Manzil Zaheer, Srinadh Bhojanapalli, Daliang Li, Felix X. Yu, Sanjiv Kumar

CoRR, 2020

Why distillation helps: a statistical perspective.

Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar

CoRR, 2020

Doubly-stochastic mining for heterogeneous retrieval.

Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar

CoRR, 2020

Why are Adaptive Methods Good for Attention Models?

Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers.

Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning discrete distributions: user vs item-level privacy.

Yuhan Liu, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Michael Riley

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Multi-Stage Influence Function.

Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane S. Boning, Cho-Jui Hsieh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust large-margin learning in hyperbolic space.

Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Federated Learning with Only Positive Labels.

Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Does label smoothing mitigate label noise?

Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Accelerating Large-Scale Inference with Anisotropic Vector Quantization.

Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Low-Rank Bottleneck in Multi-head Attention Models.

Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the 37th International Conference on Machine Learning, 2020

Are Transformers universal approximators of sequence-to-sequence functions?

Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the 8th International Conference on Learning Representations, 2020

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.

Yang You, Jing Li, Sashank J. Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, Cho-Jui Hsieh

Proceedings of the 8th International Conference on Learning Representations, 2020

Learning to Learn by Zeroth-Order Oracle.

Yangjun Ruan, Yuanhao Xiong, Sashank J. Reddi, Sanjiv Kumar, Cho-Jui Hsieh

Proceedings of the 8th International Conference on Learning Representations, 2020

Can gradient clipping mitigate label noise?

Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the 8th International Conference on Learning Representations, 2020

Pre-training Tasks for Embedding-based Large-scale Retrieval.

Wei-Cheng Chang, Felix X. Yu, Yin-Wen Chang, Yiming Yang, Sanjiv Kumar

Proceedings of the 8th International Conference on Learning Representations, 2020

Semantic Label Smoothing for Sequence to Sequence Problems.

Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 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

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy.

Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Why ADAM Beats SGD for Attention Models.

Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar, Suvrit Sra

CoRR, 2019

Online Hierarchical Clustering Approximations.

Aditya Krishna Menon, Anand Rajagopalan, Baris Sumengen, Gui Citovsky, Qin Cao, Sanjiv Kumar

CoRR, 2019

New Loss Functions for Fast Maximum Inner Product Search.

Ruiqi Guo, Quan Geng, David Simcha, Felix Chern, Sanjiv Kumar, Xiang Wu

CoRR, 2019

AdaCliP: Adaptive Clipping for Private SGD.

Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar

CoRR, 2019

Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise.

Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, Cho-Jui Hsieh

CoRR, 2019

Local Orthogonal Decomposition for Maximum Inner Product Search.

Xiang Wu, Ruiqi Guo, Sanjiv Kumar, David Simcha

CoRR, 2019

Efficient Inner Product Approximation in Hybrid Spaces.

Xiang Wu, Ruiqi Guo, David Simcha, Dave Dopson, Sanjiv Kumar

CoRR, 2019

Sampled Softmax with Random Fourier Features.

Ankit Singh Rawat, Jiecao Chen, Felix X. Yu, Ananda Theertha Suresh, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces.

Chuan Guo, Ali Mousavi, Xiang Wu, Daniel Niels Holtmann-Rice, Satyen Kale, Sashank J. Reddi, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling.

Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

Proceedings of the 36th International Conference on Machine Learning, 2019

Escaping Saddle Points with Adaptive Gradient Methods.

Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra

Proceedings of the 36th International Conference on Machine Learning, 2019

Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks.

Patrick H. Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh

Proceedings of the 7th International Conference on Learning Representations, 2019

Stochastic Negative Mining for Learning with Large Output Spaces.

Sashank J. Reddi, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Jiecao Chen, Sanjiv Kumar

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Optimal Noise-Adding Mechanism in Additive Differential Privacy.

Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Learning Adaptive Random Features.

Yanjun Li, Kai Zhang, Jun Wang, Sanjiv Kumar

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Truncated Laplacian Mechanism for Approximate Differential Privacy.

Quan Geng, Wei Ding, Ruiqi Guo, Sanjiv Kumar

CoRR, 2018

The Sparse Recovery Autoencoder.

Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

CoRR, 2018

Nonlinear Online Learning with Adaptive Nyström Approximation.

Si Si, Sanjiv Kumar, Yang Li

CoRR, 2018

Adaptive Methods for Nonconvex Optimization.

Manzil Zaheer, Sashank J. Reddi, Devendra Singh Sachan, Satyen Kale, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

cpSGD: Communication-efficient and differentially-private distributed SGD.

Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Loss Decomposition for Fast Learning in Large Output Spaces.

Ian En-Hsu Yen, Satyen Kale, Felix X. Yu, Daniel Niels Holtmann-Rice, Sanjiv Kumar, Pradeep Ravikumar

Proceedings of the 35th International Conference on Machine Learning, 2018

On the Convergence of Adam and Beyond.

Sashank J. Reddi, Satyen Kale, Sanjiv Kumar

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

On Binary Embedding using Circulant Matrices.

Felix X. Yu, Aditya Bhaskara, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang

J. Mach. Learn. Res., 2017

Now Playing: Continuous low-power music recognition.

Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo, Kevin Kilgour, Sanjiv Kumar, James Lyon, Julian Odell, Marvin Ritter, Dominik Roblek, Matthew Sharifi, Mihajlo Velimirovic

CoRR, 2017

Efficient Natural Language Response Suggestion for Smart Reply.

Matthew L. Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, László Lukács, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, Ray Kurzweil

CoRR, 2017

Multiscale Quantization for Fast Similarity Search.

Xiang Wu, Ruiqi Guo, Ananda Theertha Suresh, Sanjiv Kumar, Daniel N. Holtmann-Rice, David Simcha, Felix X. Yu

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Distributed Mean Estimation with Limited Communication.

Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan

Proceedings of the 34th International Conference on Machine Learning, 2017

Stochastic Generative Hashing.

Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning Spread-Out Local Feature Descriptors.

Xu Zhang, Felix X. Yu, Sanjiv Kumar, Shih-Fu Chang

Proceedings of the IEEE International Conference on Computer Vision, 2017

Fast Classification with Binary Prototypes.

Kai Zhong, Ruiqi Guo, Sanjiv Kumar, Bowei Yan, David Simcha, Inderjit S. Dhillon

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Learning to Hash for Indexing Big Data - A Survey.

Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang

Proc. IEEE, 2016

Software-Defined Storage-Based Data Infrastructure Supportive of Hydroclimatology Simulation Containers: A Survey.

Wonjun Lee, Sanjiv Kumar

Data Sci. Eng., 2016

A Fast Approach to Solve Matrix Games with Payoffs of Trapezoidal Fuzzy Numbers.

Sanjiv Kumar, Ritika Chopra, Ratnesh Rajan Saxena

Asia Pac. J. Oper. Res., 2016

Orthogonal Random Features.

Felix X. Yu, Ananda Theertha Suresh, Krzysztof Marcin Choromanski, Daniel N. Holtmann-Rice, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Binary embeddings with structured hashed projections.

Anna Choromanska, Krzysztof Choromanski, Mariusz Bojarski, Tony Jebara, Sanjiv Kumar, Yann LeCun

Proceedings of the 33nd International Conference on Machine Learning, 2016

Quantization based Fast Inner Product Search.

Ruiqi Guo, Sanjiv Kumar, Krzysztof Choromanski, David Simcha

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Compact Nonlinear Maps and Circulant Extensions.

Felix X. Yu, Sanjiv Kumar, Henry A. Rowley, Shih-Fu Chang

CoRR, 2015

Fast Online Clustering with Randomized Skeleton Sets.

Krzysztof Choromanski, Sanjiv Kumar, Xiaofeng Liu

CoRR, 2015

Fast Neural Networks with Circulant Projections.

Yu Cheng, Felix X. Yu, Rogério Schmidt Feris, Sanjiv Kumar, Alok N. Choudhary, Shih-Fu Chang

CoRR, 2015

A Survey of Modern Questions and Challenges in Feature Extraction.

Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

Proceedings of the 1st Workshop on Feature Extraction: Modern Questions and Challenges, 2015

Structured Transforms for Small-Footprint Deep Learning.

Vikas Sindhwani, Tara N. Sainath, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Spherical Random Features for Polynomial Kernels.

Jeffrey Pennington, Felix X. Yu, Sanjiv Kumar

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Orthogonal Projection Based on Kronecker Product.

Xu Zhang, Felix X. Yu, Ruiqi Guo, Sanjiv Kumar, Shengjin Wang, Shih-Fu Chang

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections.

Yu Cheng, Felix X. Yu, Rogério Schmidt Feris, Sanjiv Kumar, Alok N. Choudhary, Shih-Fu Chang

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

Exemplar-based large vocabulary speech recognition using k-nearest neighbors.

Yanbo Xu, Olivier Siohan, David Simcha, Sanjiv Kumar, Hank Liao

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Fast Binary Embedding for High-Dimensional Data.

Felix X. Yu, Yunchao Gong, Sanjiv Kumar

Proceedings of the Multimedia Data Mining and Analytics - Disruptive Innovation, 2015

2014

Discriminative Random Fields.

Sanjiv Kumar

Computer Vision, A Reference Guide, 2014

On Learning with Label Proportions.

Felix X. Yu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang

CoRR, 2014

Discrete Graph Hashing.

Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Circulant Binary Embedding.

Felix X. Yu, Sanjiv Kumar, Yunchao Gong, Shih-Fu Chang

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Large-scale SVD and manifold learning.

Ameet Talwalkar, Sanjiv Kumar, Mehryar Mohri, Henry A. Rowley

J. Mach. Learn. Res., 2013

\(\propto\)SVM for Learning with Label Proportions.

Felix X. Yu, Dong Liu, Sanjiv Kumar, Tony Jebara, Shih-Fu Chang

Proceedings of the 30th International Conference on Machine Learning, 2013

Learning Binary Codes for High-Dimensional Data Using Bilinear Projections.

Yunchao Gong, Sanjiv Kumar, Henry A. Rowley, Svetlana Lazebnik

Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013

2012

Semi-Supervised Hashing for Large-Scale Search.

Jun Wang, Sanjiv Kumar, Shih-Fu Chang

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Sampling Methods for the Nyström Method.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

J. Mach. Learn. Res., 2012

Angular Quantization-based Binary Codes for Fast Similarity Search.

Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Compact Hyperplane Hashing with Bilinear Functions.

Wei Liu, Jun Wang, Yadong Mu, Sanjiv Kumar, Shih-Fu Chang

Proceedings of the 29th International Conference on Machine Learning, 2012

On the Difficulty of Nearest Neighbor Search.

Junfeng He, Sanjiv Kumar, Shih-Fu Chang

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Hashing with Graphs.

Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Discriminative Graphical Models for Context-Based Classification.

Sanjiv Kumar

Proceedings of the Computer Vision: Detection, Recognition and Reconstruction, 2010

Baselines for Image Annotation.

Ameesh Makadia, Vladimir Pavlovic, Sanjiv Kumar

Int. J. Comput. Vis., 2010

Sequential Projection Learning for Hashing with Compact Codes.

Jun Wang, Sanjiv Kumar, Shih-Fu Chang

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

YouTubeCat: Learning to categorize wild web videos.

Zheshen Wang, Ming Zhao, Yang Song, Sanjiv Kumar, Baoxin Li

Proceedings of the Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, 2010

2009

Sampling Techniques for the Nystrom Method.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Ensemble Nystrom Method.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

On sampling-based approximate spectral decomposition.

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

A New Baseline for Image Annotation.

Ameesh Makadia, Vladimir Pavlovic, Sanjiv Kumar

Proceedings of the Computer Vision, 2008

Large-scale manifold learning.

Ameet Talwalkar, Sanjiv Kumar, Henry A. Rowley

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

Face tracking and recognition with visual constraints in real-world videos.

Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic, Henry A. Rowley

Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 2008

2007

Classification of Weakly-Labeled Data with Partial Equivalence Relations.

Sanjiv Kumar, Henry A. Rowley

Proceedings of the IEEE 11th International Conference on Computer Vision, 2007

2006

Discriminative Random Fields.

Sanjiv Kumar, Martial Hebert

Int. J. Comput. Vis., 2006

2005

A Hierarchical Field Framework for Unified Context-Based Classification.

Sanjiv Kumar, Martial Hebert

Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV 2005), 2005

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

Sanjiv Kumar, Jonas August, Martial Hebert

Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2005

Digital Tapestry.

Carsten Rother, Sanjiv Kumar, Vladimir Kolmogorov, Andrew Blake

Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005

2004

Path planning with hallucinated worlds.

Bart C. Nabbe, Sanjiv Kumar, Martial Hebert

Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, September 28, 2004

2003

An observation-constrained generative approach for probabilistic classification of image regions.

Sanjiv Kumar, Alexander C. Loui, Martial Hebert

Image Vis. Comput., 2003

Discriminative Fields for Modeling Spatial Dependencies in Natural Images.

Sanjiv Kumar, Martial Hebert

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

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

Sanjiv Kumar, Martial Hebert

Proceedings of the 9th IEEE International Conference on Computer Vision (ICCV 2003), 2003

Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field.

Sanjiv Kumar, Martial Hebert

Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), 2003

2000

A Fully Autonomous Microrobotic Endoscopy System.

Vijayan K. Asari, Sanjiv Kumar, Irwan M. Kassim

J. Intell. Robotic Syst., 2000

Design of a vision-guided microrobotic colonoscopy system.

Sanjiv Kumar, Irwan M. Kassim, Vijayan K. Asari

Adv. Robotics, 2000

1999

A New Approach for Nonlinear Distortion Correction in Endoscopic Images Based on Least Squares Estimation.

K. Vijayan Asari, Sanjiv Kumar, D. Radhakrishnan

IEEE Trans. Medical Imaging, 1999

A pipelined architecture for image segmentation by adaptive progressive thresholding.

K. Vijayan Asari, Thambipillai Srikanthan, Sanjiv Kumar, D. Radhakrishnan

Microprocess. Microsystems, 1999