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2020 – today
- 2024
[j17]Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
What do larger image classifiers memorise? Trans. Mach. Learn. Res. 2024 (2024)- [c110]Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar:
Rethinking FID: Towards a Better Evaluation Metric for Image Generation. CVPR 2024: 9307-9315 - [c109]Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar:
MarkovGen: Structured Prediction for Efficient Text-to-Image Generation. CVPR 2024: 9316-9325 - [c108]Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar:
On Bias-Variance Alignment in Deep Models. ICLR 2024 - [c107]Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan:
Think before you speak: Training Language Models With Pause Tokens. ICLR 2024 - [c106]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-Level Uncertainty And Beyond. ICLR 2024 - [c105]Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontañón, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli:
Functional Interpolation for Relative Positions improves Long Context Transformers. ICLR 2024 - [c104]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Neha Gupta, Sanjiv Kumar:
Learning to Reject Meets Long-tail Learning. ICLR 2024 - [c103]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Plugin estimators for selective classification with out-of-distribution detection. ICLR 2024 - [c102]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. ICLR 2024 - [c101]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. ICLR 2024 - [c100]Khashayar Gatmiry, Nikunj Saunshi, Sashank J. Reddi, Stefanie Jegelka, Sanjiv Kumar:
Can Looped Transformers Learn to Implement Multi-step Gradient Descent for In-context Learning? ICML 2024 - [c99]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. ICML 2024 - [c98]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. ICML 2024 - [c97]Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
Tandem Transformers for Inference Efficient LLMs. ICML 2024 - [i105]Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar:
Rethinking FID: Towards a Better Evaluation Metric for Image Generation. CoRR abs/2401.09603 (2024) - [i104]Ke Ye, Heinrich Jiang, Afshin Rostamizadeh, Ayan Chakrabarti, Giulia DeSalvo, Jean-François Kagy, Lazaros Karydas, Gui Citovsky, Sanjiv Kumar:
SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection. CoRR abs/2401.13160 (2024) - [i103]Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu, Sobhan Miryoosefi, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar:
Efficient Stagewise Pretraining via Progressive Subnetworks. CoRR abs/2402.05913 (2024) - [i102]Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
Tandem Transformers for Inference Efficient LLMs. CoRR abs/2402.08644 (2024) - [i101]Yashas Samaga, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. CoRR abs/2402.09360 (2024) - [i100]Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix X. Yu, Sanjiv Kumar:
Metric-aware LLM inference. CoRR abs/2403.04182 (2024) - [i99]Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar:
SOAR: Improved Indexing for Approximate Nearest Neighbor Search. CoRR abs/2404.00774 (2024) - [i98]Taehyeon Kim, Ananda Theertha Suresh, Kishore Papineni, Michael Riley, Sanjiv Kumar, Adrian Benton:
Towards Fast Inference: Exploring and Improving Blockwise Parallel Drafts. CoRR abs/2404.09221 (2024) - [i97]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-level uncertainty and beyond. CoRR abs/2404.10136 (2024) - [i96]Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar:
Faster Cascades via Speculative Decoding. CoRR abs/2405.19261 (2024) - [i95]Stefani Karp, Nikunj Saunshi, Sobhan Miryoosefi, Sashank J. Reddi, Sanjiv Kumar:
Landscape-Aware Growing: The Power of a Little LAG. CoRR abs/2406.02469 (2024) - [i94]Ziwei Ji, Himanshu Jain, Andreas Veit, Sashank J. Reddi, Sadeep Jayasumana, Ankit Singh Rawat, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar:
Efficient Document Ranking with Learnable Late Interactions. CoRR abs/2406.17968 (2024) - [i93]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. CoRR abs/2407.21046 (2024) - [i92]Nikunj Saunshi, Stefani Karp, Shankar Krishnan, Sobhan Miryoosefi, Sashank J. Reddi, Sanjiv Kumar:
On the Inductive Bias of Stacking Towards Improving Reasoning. CoRR abs/2409.19044 (2024) - 2023
- [c96]Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, Sanjiv Kumar:
Large Language Models with Controllable Working Memory. ACL (Findings) 2023: 1774-1793 - [c95]Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang:
Leveraging Importance Weights in Subset Selection. ICLR 2023 - [c94]Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar:
Supervision Complexity and its Role in Knowledge Distillation. ICLR 2023 - [c93]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:
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers. ICLR 2023 - [c92]Si Si, Felix X. Yu, Ankit Singh Rawat, Cho-Jui Hsieh, Sanjiv Kumar:
Serving Graph Compression for Graph Neural Networks. ICLR 2023 - [c91]Philip Sun, Ruiqi Guo, Sanjiv Kumar:
Automating Nearest Neighbor Search Configuration with Constrained Optimization. ICLR 2023 - [c90]Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar:
Teacher Guided Training: An Efficient Framework for Knowledge Transfer. ICLR 2023 - [c89]Sashank J. Reddi, Sobhan Miryoosefi, Stefani Karp, Shankar Krishnan, Satyen Kale, Seungyeon Kim, Sanjiv Kumar:
Efficient Training of Language Models using Few-Shot Learning. ICML 2023: 14553-14568 - [c88]Guna Shekar M., Wonjun Lee, Sanjiv Kumar, Yanan Duan
, Imtiaz Rangwala:
A Framework for Developing the Next Generation Interactive Soil Moisture Forecasting System Using the Long-Short Term Memory Model. ICMLA 2023: 1986-1993 - [c87]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? NeurIPS 2023 - [c86]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? NeurIPS 2023 - [c85]Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar:
SOAR: Improved Indexing for Approximate Nearest Neighbor Search. NeurIPS 2023 - [c84]Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar:
ResMem: Learn what you can and memorize the rest. NeurIPS 2023 - [i91]Philip Sun, Ruiqi Guo, Sanjiv Kumar:
Automating Nearest Neighbor Search Configuration with Constrained Optimization. CoRR abs/2301.01702 (2023) - [i90]Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Sadeep Jayasumana, Veeranjaneyulu Sadhanala, Wittawat Jitkrittum, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar:
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. CoRR abs/2301.12005 (2023) - [i89]Gui Citovsky, Giulia DeSalvo, Sanjiv Kumar, Srikumar Ramalingam, Afshin Rostamizadeh, Yunjuan Wang:
Leveraging Importance Weights in Subset Selection. CoRR abs/2301.12052 (2023) - [i88]Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar:
Supervision Complexity and its Role in Knowledge Distillation. CoRR abs/2301.12245 (2023) - [i87]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Learning to reject meets OOD detection: Are all abstentions created equal? CoRR abs/2301.12386 (2023) - [i86]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? CoRR abs/2301.12923 (2023) - [i85]Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar:
ResMem: Learn what you can and memorize the rest. CoRR abs/2302.01576 (2023) - [i84]Samy Jelassi, Boris Hanin, Ziwei Ji, Sashank J. Reddi, Srinadh Bhojanapalli, Sanjiv Kumar:
Depth Dependence of μP Learning Rates in ReLU MLPs. CoRR abs/2305.07810 (2023) - [i83]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? CoRR abs/2307.02764 (2023) - [i82]Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar:
SPEGTI: Structured Prediction for Efficient Generative Text-to-Image Models. CoRR abs/2308.10997 (2023) - [i81]Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan:
Think before you speak: Training Language Models With Pause Tokens. CoRR abs/2310.02226 (2023) - [i80]Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontañón, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli:
Functional Interpolation for Relative Positions Improves Long Context Transformers. CoRR abs/2310.04418 (2023) - [i79]Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
What do larger image classifiers memorise? CoRR abs/2310.05337 (2023) - [i78]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. CoRR abs/2310.08461 (2023) - [i77]Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar:
It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models. CoRR abs/2310.09250 (2023) - [i76]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:
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent. CoRR abs/2312.10003 (2023) - [i75]Srikumar Ramalingam, Pranjal Awasthi, Sanjiv Kumar:
A Weighted K-Center Algorithm for Data Subset Selection. CoRR abs/2312.10602 (2023) - 2022
- [j16]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. Trans. Mach. Learn. Res. 2022 (2022) - [c83]Gautam Gautam, Neeraj Sharma, Sanjiv Kumar:
Radio over FSO System for 5G Wireless Communication. ICCCNT 2022: 1-6 - [c82]Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks Using Scale Invariant Architectures. ICML 2022: 12656-12684 - [c81]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar:
In defense of dual-encoders for neural ranking. ICML 2022: 15376-15400 - [c80]Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar:
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s. NeurIPS 2022 - [c79]Zonglin Li, Ruiqi Guo, Sanjiv Kumar:
Decoupled Context Processing for Context Augmented Language Modeling. NeurIPS 2022 - [c78]Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Post-hoc estimators for learning to defer to an expert. NeurIPS 2022 - [i74]Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer, Sashank J. Reddi, Sanjiv Kumar:
Robust Training of Neural Networks using Scale Invariant Architectures. CoRR abs/2202.00980 (2022) - [i73]Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar:
Predicting on the Edge: Identifying Where a Larger Model Does Better. CoRR abs/2202.07652 (2022) - [i72]Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
ELM: Embedding and Logit Margins for Long-Tail Learning. CoRR abs/2204.13208 (2022) - [i71]Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer, Sanjiv Kumar:
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s. CoRR abs/2206.14286 (2022) - [i70]Manzil Zaheer, Ankit Singh Rawat, Seungyeon Kim, Chong You, Himanshu Jain, Andreas Veit, Rob Fergus, Sanjiv Kumar:
Teacher Guided Training: An Efficient Framework for Knowledge Transfer. CoRR abs/2208.06825 (2022) - [i69]Zonglin Li, Ruiqi Guo, Sanjiv Kumar:
Decoupled Context Processing for Context Augmented Language Modeling. CoRR abs/2210.05758 (2022) - [i68]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:
Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers. CoRR abs/2210.06313 (2022) - [i67]Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit, Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar:
When does mixup promote local linearity in learned representations? CoRR abs/2210.16413 (2022) - [i66]Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix X. Yu, Cho-Jui Hsieh, Inderjit S. Dhillon, Sanjiv Kumar:
Preserving In-Context Learning ability in Large Language Model Fine-tuning. CoRR abs/2211.00635 (2022) - [i65]Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix X. Yu, Sanjiv Kumar:
Large Language Models with Controllable Working Memory. CoRR abs/2211.05110 (2022) - 2021
- [j15]Ankita Verma, Savita, Sanjiv Kumar:
Routing Protocols in Delay Tolerant Networks: Comparative and Empirical Analysis. Wirel. Pers. Commun. 118(1): 551-574 (2021) - [c77]Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar:
RankDistil: Knowledge Distillation for Ranking. AISTATS 2021: 2368-2376 - [c76]Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, Cho-Jui Hsieh:
Evaluations and Methods for Explanation through Robustness Analysis. ICLR 2021 - [c75]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. ICLR 2021 - [c74]Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Overparameterisation and worst-case generalisation: friend or foe? ICLR 2021 - [c73]Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konecný, Sanjiv Kumar, Hugh Brendan McMahan:
Adaptive Federated Optimization. ICLR 2021 - [c72]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. ICLR 2021 - [c71]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
A statistical perspective on distillation. ICML 2021: 7632-7642 - [c70]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. ICML 2021: 8890-8901 - [c69]Erik Lindgren, Sashank J. Reddi, Ruiqi Guo, Sanjiv Kumar:
Efficient Training of Retrieval Models using Negative Cache. NeurIPS 2021: 4134-4146 - [c68]Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar:
Batch Active Learning at Scale. NeurIPS 2021: 11933-11944 - [i64]Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Krishna Menon, Sanjiv Kumar:
On the Reproducibility of Neural Network Predictions. CoRR abs/2102.03349 (2021) - [i63]Srikumar Ramalingam, Daniel Glasner, Kaushal Patel, Raviteja Vemulapalli, Sadeep Jayasumana, Sanjiv Kumar:
Balancing Constraints and Submodularity in Data Subset Selection. CoRR abs/2104.12835 (2021) - [i62]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces. CoRR abs/2105.05736 (2021) - [i61]Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam, Cho-Jui Hsieh, Kishore Papineni, Sanjiv Kumar:
Balancing Robustness and Sensitivity using Feature Contrastive Learning. CoRR abs/2105.09394 (2021) - [i60]Baris Sumengen, Anand Rajagopalan, Gui Citovsky, David Simcha, Olivier Bachem, Pradipta Mitra, Sam Blasiak, Mason Liang, Sanjiv Kumar:
Scaling Hierarchical Agglomerative Clustering to Billion-sized Datasets. CoRR abs/2105.11653 (2021) - [i59]Srinadh Bhojanapalli, Ayan Chakrabarti, Himanshu Jain, Sanjiv Kumar, Michal Lukasik, Andreas Veit:
Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation. CoRR abs/2106.08823 (2021) - [i58]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. CoRR abs/2106.10494 (2021) - [i57]Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, Sanjiv Kumar:
Batch Active Learning at Scale. CoRR abs/2107.14263 (2021) - [i56]Srinadh Bhojanapalli, Ayan Chakrabarti, Andreas Veit, Michal Lukasik, Himanshu Jain, Frederick Liu, Yin-Wen Chang, Sanjiv Kumar:
Leveraging redundancy in attention with Reuse Transformers. CoRR abs/2110.06821 (2021) - [i55]Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar:
When in Doubt, Summon the Titans: Efficient Inference with Large Models. CoRR abs/2110.10305 (2021) - 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): 1650047:1-1650047:14 (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]Sanjiv Kumar, Martial Hebert:
Discriminative Fields for Modeling Spatial Dependencies in Natural Images. NIPS 2003: 1531-1538 - 2000
- [j4]Sanjiv Kumar, Irwan M. Kassim, Vijayan K. Asari:
Design of a vision-guided microrobotic colonoscopy system. Adv. Robotics 14(2): 87-104 (2000) - [j3]Vijayan K. Asari, Sanjiv Kumar, Irwan M. Kassim:
A Fully Autonomous Microrobotic Endoscopy System. J. Intell. Robotic Syst. 28(4): 325-341 (2000)
1990 – 1999
- 1999
- [j2]K. Vijayan Asari, Thambipillai Srikanthan, Sanjiv Kumar, D. Radhakrishnan:
A pipelined architecture for image segmentation by adaptive progressive thresholding. Microprocess. Microsystems 23(8-9): 493-499 (1999) - [j1]K. Vijayan Asari, Sanjiv Kumar, D. Radhakrishnan:
A New Approach for Nonlinear Distortion Correction in Endoscopic Images Based on Least Squares Estimation. IEEE Trans. Medical Imaging 18(4): 345-354 (1999)
Coauthor Index
Daniel Niels Holtmann-Rice
aka: Daniel N. Holtmann-Rice
aka: Daniel N. Holtmann-Rice
[j17] [c107] [c106] [c104] [c103] [c101] [c99] [i100] [i97] [i96] [i94] [c94] [c87] [c86] [c84] [i90] [i88] [i87] [i86] [i85] [i83] [i81] [i79] [i78] [j16] [c81] [c78] [i72] [i67] [c77] [c75] [c74] [c72] [c71] [c70] [i64] [i62] [i58] [i55] [c65] [c63] [c57] [c56] [c55] [i51] [i50] [i49] [i48] [i47] [i44] [i41] [i40] [i29]
[j17] [c107] [c106] [c101] [c99] [i97] [i96] [i94] [c96] [c94] [c93] [c92] [c90] [c87] [c84] [i90] [i88] [i85] [i83] [i81] [i79] [i78] [c81] [c78] [i72] [i70] [i68] [i65] [c77] [c75] [c74] [c71] [c70] [i64] [i62] [i55] [c63] [c60] [c59] [c56] [c55] [c52] [i53] [i50] [i49] [i48] [i47] [i45] [i44] [i39] [c43] [i32] [i26]
[i100] [c96] [c93] [c92] [i76] [i68] [i66] [i65] [c77] [c70] [i62] [c65] [c64] [c56] [c53] [i54] [i49] [i48] [i42] [i41] [i39] [c48] [c45] [c43] [i32] [i31] [c41] [c40] [i24] [i23] [i20] [j14] [c37] [c35] [c34] [i16] [c31] [i14] [i13] [c29] [c28] [c26] [p2] [i12] [i11] [i5] [c24] [i4] [i3] [c21] [i2]
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