A pipelined architecture for image segmentation by adaptive progressive thresholding. - K. Vijayan Asari; Thambipillai Srikanthan; Sanjiv Kumar; D. Radhakrishnan (Microprocess. Microsystems, 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)

Design of a vision-guided microrobotic colonoscopy system. - Sanjiv Kumar; Irwan M. Kassim; Vijayan K. Asari (Adv. Robotics, 2000)

A Fully Autonomous Microrobotic Endoscopy System. - Vijayan K. Asari; Sanjiv Kumar; Irwan M. Kassim (J. Intell. Robotic Syst., 2000)

Man-Made Structure Detection in Natural Images using a Causal Multiscale Random Field. - Sanjiv Kumar; Martial Hebert (CVPR (1), 2003)

Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification. - Sanjiv Kumar; Martial Hebert (ICCV, 2003)

Discriminative Fields for Modeling Spatial Dependencies in Natural Images. - Sanjiv Kumar; Martial Hebert (NIPS, 2003)

An observation-constrained generative approach for probabilistic classification of image regions. - Sanjiv Kumar; Alexander C. Loui; Martial Hebert (Image Vis. Comput., 2003)

Path planning with hallucinated worlds. - Bart C. Nabbe; Sanjiv Kumar; Martial Hebert (IROS, 2004)

Digital Tapestry. - Carsten Rother; Sanjiv Kumar; Vladimir Kolmogorov; Andrew Blake 001 (CVPR (1), 2005)

Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study. - Sanjiv Kumar; Jonas August; Martial Hebert (EMMCVPR, 2005)

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

Discriminative Random Fields. - Sanjiv Kumar; Martial Hebert (Int. J. Comput. Vis., 2006)

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

Face tracking and recognition with visual constraints in real-world videos. - Minyoung Kim 001; Sanjiv Kumar; Vladimir Pavlovic 001; Henry A. Rowley (CVPR, 2008)

Large-scale manifold learning. - Ameet Talwalkar; Sanjiv Kumar; Henry A. Rowley (CVPR, 2008)

A New Baseline for Image Annotation. - Ameesh Makadia; Vladimir Pavlovic 001; Sanjiv Kumar (ECCV (3), 2008)

On sampling-based approximate spectral decomposition. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (ICML, 2009)

Ensemble Nystrom Method. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (NIPS, 2009)

Sampling Techniques for the Nystrom Method. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (AISTATS, 2009)

YouTubeCat: Learning to categorize wild web videos. - Zheshen Wang; Ming Zhao 003; Yang Song 009; Sanjiv Kumar; Baoxin Li (CVPR, 2010)

Sequential Projection Learning for Hashing with Compact Codes. - Jun Wang 006; Sanjiv Kumar; Shih-Fu Chang (ICML, 2010)

Baselines for Image Annotation. - Ameesh Makadia; Vladimir Pavlovic 001; Sanjiv Kumar (Int. J. Comput. Vis., 2010)

Discriminative Graphical Models for Context-Based Classification. - Sanjiv Kumar (Computer Vision: Detection, Recognition and Reconstruction, 2010)

Hashing with Graphs. - Wei Liu 005; Jun Wang 006; Sanjiv Kumar; Shih-Fu Chang (ICML, 2011)

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

Compact Hyperplane Hashing with Bilinear Functions. - Wei Liu 005; Jun Wang 006; Yadong Mu; Sanjiv Kumar; Shih-Fu Chang (ICML, 2012)

Angular Quantization-based Binary Codes for Fast Similarity Search. - Yunchao Gong; Sanjiv Kumar; Vishal Verma; Svetlana Lazebnik (NIPS, 2012)

Sampling Methods for the Nyström Method. - Sanjiv Kumar; Mehryar Mohri; Ameet Talwalkar (J. Mach. Learn. Res., 2012)

Semi-Supervised Hashing for Large-Scale Search. - Jun Wang 006; Sanjiv Kumar; Shih-Fu Chang (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

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

( propto )SVM for Learning with Label Proportions. - Felix X. Yu; Dong Liu 001; Sanjiv Kumar; Tony Jebara; Shih-Fu Chang (ICML (3), 2013)

Large-scale SVD and manifold learning. - Ameet Talwalkar; Sanjiv Kumar; Mehryar Mohri; Henry A. Rowley (J. Mach. Learn. Res., 2013)

Circulant Binary Embedding. - Felix X. Yu; Sanjiv Kumar; Yunchao Gong; Shih-Fu Chang (ICML, 2014)

Discrete Graph Hashing. - Wei Liu 005; Cun Mu; Sanjiv Kumar; Shih-Fu Chang (NIPS, 2014)

On Learning with Label Proportions. - Felix X. Yu; Sanjiv Kumar; Tony Jebara; Shih-Fu Chang (CoRR, 2014)

Discriminative Random Fields. - Sanjiv Kumar (Computer Vision, A Reference Guide, 2014)

Fast Binary Embedding for High-Dimensional Data. - Felix X. Yu; Yunchao Gong; Sanjiv Kumar (Multimedia Data Mining and Analytics, 2015)

Exemplar-based large vocabulary speech recognition using k-nearest neighbors. - Yanbo Xu; Olivier Siohan; David Simcha; Sanjiv Kumar; Hank Liao (ICASSP, 2015)

An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections. - Yu Cheng 001; Felix X. Yu; Rogério Schmidt Feris; Sanjiv Kumar; Alok N. Choudhary; Shih-Fu Chang (ICCV, 2015)

Fast Orthogonal Projection Based on Kronecker Product. - Xu Zhang 022; Felix X. Yu; Ruiqi Guo; Sanjiv Kumar; Shengjin Wang; Shih-Fu Chang (ICCV, 2015)

Spherical Random Features for Polynomial Kernels. - Jeffrey Pennington; Felix X. Yu; Sanjiv Kumar (NIPS, 2015)

Structured Transforms for Small-Footprint Deep Learning. - Vikas Sindhwani; Tara N. Sainath; Sanjiv Kumar (NIPS, 2015)

A Survey of Modern Questions and Challenges in Feature Extraction. - Dmitry Storcheus; Afshin Rostamizadeh; Sanjiv Kumar (FE@NIPS, 2015)

Fast Neural Networks with Circulant Projections. - Yu Cheng 001; Felix X. Yu; Rogério Schmidt Feris; Sanjiv Kumar; Alok N. Choudhary; Shih-Fu Chang (CoRR, 2015)

Fast Online Clustering with Randomized Skeleton Sets. - Krzysztof Choromanski; Sanjiv Kumar; Xiaofeng Liu (CoRR, 2015)

Compact Nonlinear Maps and Circulant Extensions. - Felix X. Yu; Sanjiv Kumar; Henry A. Rowley; Shih-Fu Chang (CoRR, 2015)

Quantization based Fast Inner Product Search. - Ruiqi Guo; Sanjiv Kumar; Krzysztof Choromanski; David Simcha (AISTATS, 2016)

Binary embeddings with structured hashed projections. - Anna Choromanska; Krzysztof Choromanski; Mariusz Bojarski; Tony Jebara; Sanjiv Kumar; Yann LeCun (ICML, 2016)

Orthogonal Random Features. - Felix X. Yu; Ananda Theertha Suresh; Krzysztof Marcin Choromanski; Daniel N. Holtmann-Rice; Sanjiv Kumar (NIPS, 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)

Software-Defined Storage-Based Data Infrastructure Supportive of Hydroclimatology Simulation Containers: A Survey. - Wonjun Lee 003; Sanjiv Kumar (Data Sci. Eng., 2016)

Learning to Hash for Indexing Big Data - A Survey. - Jun Wang 006; Wei Liu 005; Sanjiv Kumar; Shih-Fu Chang (Proc. IEEE, 2016)

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

Learning Spread-Out Local Feature Descriptors. - Xu Zhang 022; Felix X. Yu; Sanjiv Kumar; Shih-Fu Chang (ICCV, 2017)

Stochastic Generative Hashing. - Bo Dai 001; Ruiqi Guo; Sanjiv Kumar; Niao He; Le Song (ICML, 2017)

Distributed Mean Estimation with Limited Communication. - Ananda Theertha Suresh; Felix X. Yu; Sanjiv Kumar; H. Brendan McMahan (ICML, 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 (NIPS, 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)

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)

On Binary Embedding using Circulant Matrices. - Felix X. Yu; Aditya Bhaskara; Sanjiv Kumar; Yunchao Gong; Shih-Fu Chang (J. Mach. Learn. Res., 2017)

On the Convergence of Adam and Beyond. - Sashank J. Reddi; Satyen Kale; Sanjiv Kumar (ICLR, 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 (ICML, 2018)

cpSGD: Communication-efficient and differentially-private distributed SGD. - Naman Agarwal; Ananda Theertha Suresh; Felix X. Yu; Sanjiv Kumar; Brendan McMahan (NeurIPS, 2018)

Adaptive Methods for Nonconvex Optimization. - Manzil Zaheer; Sashank J. Reddi; Devendra Singh Sachan; Satyen Kale; Sanjiv Kumar (NeurIPS, 2018)

Nonlinear Online Learning with Adaptive Nyström Approximation. - Si Si; Sanjiv Kumar; Yang Li 058 (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)

Truncated Laplacian Mechanism for Approximate Differential Privacy. - Quan Geng; Wei Ding; Ruiqi Guo; Sanjiv Kumar (CoRR, 2018)

Learning Adaptive Random Features. - Yanjun Li; Kai Zhang; Jun Wang; Sanjiv Kumar (AAAI, 2019)

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

Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks. - Patrick H. Chen; Si Si; Sanjiv Kumar; Yang Li 058; Cho-Jui Hsieh (ICLR (Poster), 2019)

Escaping Saddle Points with Adaptive Gradient Methods. - Matthew Staib; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar; Suvrit Sra (ICML, 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 (ICML, 2019)

Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces. - Chuan Guo 001; Ali Mousavi 003; Xiang Wu; Daniel Niels Holtmann-Rice; Satyen Kale; Sashank J. Reddi; Sanjiv Kumar (NeurIPS, 2019)

Sampled Softmax with Random Fourier Features. - Ankit Singh Rawat; Jiecao Chen; Felix X. Yu; Ananda Theertha Suresh; Sanjiv Kumar (NeurIPS, 2019)

Efficient Inner Product Approximation in Hybrid Spaces. - Xiang Wu; Ruiqi Guo; David Simcha; Dave Dopson; Sanjiv Kumar (CoRR, 2019)

Local Orthogonal Decomposition for Maximum Inner Product Search. - Xiang Wu; Ruiqi Guo; Sanjiv Kumar; David Simcha (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)

AdaCliP: Adaptive Clipping for Private SGD. - Venkatadheeraj Pichapati; Ananda Theertha Suresh; Felix X. Yu; Sashank J. Reddi; 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)

Online Hierarchical Clustering Approximations. - Aditya Krishna Menon; Anand Rajagopalan; Baris Sumengen; Gui Citovsky; Qin Cao; Sanjiv Kumar (CoRR, 2019)

Why ADAM Beats SGD for Attention Models. - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (CoRR, 2019)

Tight Analysis of Privacy and Utility Tradeoff in Approximate Differential Privacy. - Quan Geng; Wei Ding; Ruiqi Guo; Sanjiv Kumar (AISTATS, 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 (CVPR, 2020)

Semantic Label Smoothing for Sequence to Sequence Problems. - Michal Lukasik; Himanshu Jain; Aditya Krishna Menon; Seungyeon Kim 001; Srinadh Bhojanapalli; Felix X. Yu; Sanjiv Kumar (EMNLP (1), 2020)

Pre-training Tasks for Embedding-based Large-scale Retrieval. - Wei-Cheng Chang; Felix X. Yu; Yin-Wen Chang; Yiming Yang; Sanjiv Kumar (ICLR, 2020)

Can gradient clipping mitigate label noise? - Aditya Krishna Menon; Ankit Singh Rawat; Sashank J. Reddi; Sanjiv Kumar (ICLR, 2020)

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

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. - Yang You 001; Jing Li; Sashank J. Reddi; Jonathan Hseu; Sanjiv Kumar; Srinadh Bhojanapalli; Xiaodan Song; James Demmel; Kurt Keutzer; Cho-Jui Hsieh (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)

Low-Rank Bottleneck in Multi-head Attention Models. - Srinadh Bhojanapalli; Chulhee Yun; Ankit Singh Rawat; Sashank J. Reddi; 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 (ICML, 2020)

Does label smoothing mitigate label noise? - Michal Lukasik; Srinadh Bhojanapalli; Aditya Krishna Menon; Sanjiv Kumar (ICML, 2020)

Federated Learning with Only Positive Labels. - Felix X. Yu; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar (ICML, 2020)

Robust large-margin learning in hyperbolic space. - Melanie Weber 001; Manzil Zaheer; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar (NeurIPS, 2020)

Multi-Stage Influence Function. - Hongge Chen; Si Si; Yang Li 058; Ciprian Chelba; Sanjiv Kumar; Duane S. Boning; Cho-Jui Hsieh (NeurIPS, 2020)

Learning discrete distributions: user vs item-level privacy. - Yuhan Liu; Ananda Theertha Suresh; Felix X. Yu; Sanjiv Kumar; Michael Riley 001 (NeurIPS, 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 (NeurIPS, 2020)

Why are Adaptive Methods Good for Attention Models? - Jingzhao Zhang; Sai Praneeth Karimireddy; Andreas Veit; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar; Suvrit Sra (NeurIPS, 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 distillation helps: a statistical perspective. - Aditya Krishna Menon; Ankit Singh Rawat; Sashank J. Reddi; Seungyeon Kim 001; Sanjiv Kumar (CoRR, 2020)

Modifying Memories in Transformer Models. - Chen Zhu 001; Ankit Singh Rawat; Manzil Zaheer; Srinadh Bhojanapalli; Daliang Li; Felix X. Yu; Sanjiv Kumar (CoRR, 2020)

Kernelized Classification in Deep Networks. - Sadeep Jayasumana; Srikumar Ramalingam; Sanjiv Kumar (CoRR, 2020)

RankDistil: Knowledge Distillation for Ranking. - Sashank J. Reddi; Rama Kumar Pasumarthi; Aditya Krishna Menon; Ankit Singh Rawat; Felix X. Yu; Seungyeon Kim 001; Andreas Veit; Sanjiv Kumar (AISTATS, 2021)

Evaluations and Methods for Explanation through Robustness Analysis. - Cheng-Yu Hsieh; Chih-Kuan Yeh; Xuanqing Liu; Pradeep Kumar Ravikumar; Seungyeon Kim 001; 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 (ICLR, 2021)

Adaptive Federated Optimization. - Sashank J. Reddi; Zachary Charles; Manzil Zaheer; Zachary Garrett; Keith Rush; Jakub Konecný; Sanjiv Kumar; Hugh Brendan McMahan (ICLR, 2021)

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

A statistical perspective on distillation. - Aditya Krishna Menon; Ankit Singh Rawat; Sashank J. Reddi; Seungyeon Kim 001; Sanjiv Kumar (ICML, 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 (ICML, 2021)

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

Efficient Training of Retrieval Models using Negative Cache. - Erik Lindgren; Sashank J. Reddi; Ruiqi Guo; Sanjiv Kumar (NeurIPS, 2021)

On the Reproducibility of Neural Network Predictions. - Srinadh Bhojanapalli; Kimberly Wilber; Andreas Veit; Ankit Singh Rawat; Seungyeon Kim 001; Aditya Krishna Menon; 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)

Balancing Robustness and Sensitivity using Feature Contrastive Learning. - Seungyeon Kim 001; Daniel Glasner; Srikumar Ramalingam; Cho-Jui Hsieh; Kishore Papineni; Sanjiv Kumar (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)

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)

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)

When in Doubt, Summon the Titans: Efficient Inference with Large Models. - Ankit Singh Rawat; Manzil Zaheer; Aditya Krishna Menon; Amr Ahmed 001; Sanjiv Kumar (CoRR, 2021)

Routing Protocols in Delay Tolerant Networks: Comparative and Empirical Analysis. - Ankita Verma; Savita; Sanjiv Kumar (Wirel. Pers. Commun., 2021)

Radio over FSO System for 5G Wireless Communication. - Gautam Gautam; Neeraj Sharma; Sanjiv Kumar (ICCCNT, 2022)

Robust Training of Neural Networks Using Scale Invariant Architectures. - Zhiyuan Li 005; Srinadh Bhojanapalli; Manzil Zaheer; Sashank J. Reddi; Sanjiv Kumar (ICML, 2022)

In defense of dual-encoders for neural ranking. - Aditya Krishna Menon; Sadeep Jayasumana; Ankit Singh Rawat; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar (ICML, 2022)

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

Decoupled Context Processing for Context Augmented Language Modeling. - Zonglin Li; Ruiqi Guo; Sanjiv Kumar (NeurIPS, 2022)

Post-hoc estimators for learning to defer to an expert. - Harikrishna Narasimhan; Wittawat Jitkrittum; Aditya Krishna Menon; Ankit Singh Rawat; Sanjiv Kumar (NeurIPS, 2022)

Predicting on the Edge: Identifying Where a Larger Model Does Better. - Taman Narayan; Heinrich Jiang; Sen Zhao; 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)

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)

When does mixup promote local linearity in learned representations? - Arslan Chaudhry; Aditya Krishna Menon; Andreas Veit; Sadeep Jayasumana; Srikumar Ramalingam; 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)

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 016; Michal Lukasik; Andreas Veit; Felix X. Yu; Sanjiv Kumar (ACL (Findings), 2023)

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

Supervision Complexity and its Role in Knowledge Distillation. - Hrayr Harutyunyan; Ankit Singh Rawat; Aditya Krishna Menon; Seungyeon Kim 001; 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 X. Yu; Ruiqi Guo; Sanjiv Kumar (ICLR, 2023)

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

Automating Nearest Neighbor Search Configuration with Constrained Optimization. - Philip Sun; Ruiqi Guo; Sanjiv Kumar (ICLR, 2023)

Teacher Guided Training: An Efficient Framework for Knowledge Transfer. - Manzil Zaheer; Ankit Singh Rawat; Seungyeon Kim 001; Chong You; Himanshu Jain; Andreas Veit; Rob Fergus; Sanjiv Kumar (ICLR, 2023)

Efficient Training of Language Models using Few-Shot Learning. - Sashank J. Reddi; Sobhan Miryoosefi; Stefani Karp; Shankar Krishnan; Satyen Kale; Seungyeon Kim 001; Sanjiv Kumar (ICML, 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 (ICMLA, 2023)

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

On student-teacher deviations in distillation: does it pay to disobey? - Vaishnavh Nagarajan; Aditya Krishna Menon; Srinadh Bhojanapalli; Hossein Mobahi; Sanjiv Kumar (NeurIPS, 2023)

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

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Sadeep Jayasumana; Veeranjaneyulu Sadhanala; Wittawat Jitkrittum; Aditya Krishna Menon; Rob Fergus; 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)

Depth Dependence of μP Learning Rates in ReLU MLPs. - Samy Jelassi; Boris Hanin; Ziwei Ji; Sashank J. Reddi; Srinadh Bhojanapalli; Sanjiv Kumar (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)

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)

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; et al. (CoRR, 2023)

A Weighted K-Center Algorithm for Data Subset Selection. - Srikumar Ramalingam; Pranjal Awasthi; Sanjiv Kumar (CoRR, 2023)

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

Regression Aware Inference with LLMs. - Michal Lukasik; Harikrishna Narasimhan; Aditya Krishna Menon; Felix X. Yu; Sanjiv Kumar (EMNLP (Findings), 2024)

On Bias-Variance Alignment in Deep Models. - Lin Chen; Michal Lukasik; Wittawat Jitkrittum; Chong You; Sanjiv Kumar (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)

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

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 (ICLR, 2024)

Learning to Reject Meets Long-tail Learning. - Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Neha Gupta; Sanjiv Kumar (ICLR, 2024)

Plugin estimators for selective classification with out-of-distribution detection. - Harikrishna Narasimhan; Aditya Krishna Menon; Wittawat Jitkrittum; Sanjiv Kumar (ICLR, 2024)

Two-stage LLM Fine-tuning with Less Specialization and More Generalization. - Yihan Wang; Si Si; Daliang Li; Michal Lukasik; Felix X. Yu; Cho-Jui Hsieh; Inderjit S. Dhillon; 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 (ICLR, 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 (ICML, 2024)

USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Wittawat Jitkrittum; Veeranjaneyulu Sadhanala; Sadeep Jayasumana; Aditya Krishna Menon; Rob Fergus; 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 (ICML, 2024)

Tandem Transformers for Inference Efficient LLMs. - Aishwarya P. S.; Pranav Ajit Nair; Yashas Samaga; Toby Boyd; Sanjiv Kumar; Prateek Jain 002; Praneeth Netrapalli (ICML, 2024)

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

On the Inductive Bias of Stacking Towards Improving Reasoning. - Nikunj Saunshi; Stefani Karp; Shankar Krishnan; Sobhan Miryoosefi; Sashank Jakkam Reddi; Sanjiv Kumar (NeurIPS, 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)

HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. - Yashas Samaga; Varun Yerram; Chong You; Srinadh Bhojanapalli; Sanjiv Kumar; Prateek Jain 002; Praneeth Netrapalli (CoRR, 2024)

Metric-aware LLM inference. - Michal Lukasik; Harikrishna Narasimhan; Aditya Krishna Menon; Felix X. Yu; Sanjiv Kumar (CoRR, 2024)

Towards Fast Inference: Exploring and Improving Blockwise Parallel Drafts. - Taehyeon Kim; Ananda Theertha Suresh; Kishore Papineni; Michael Riley 001; Sanjiv Kumar; Adrian Benton (CoRR, 2024)

Landscape-Aware Growing: The Power of a Little LAG. - Stefani Karp; Nikunj Saunshi; Sobhan Miryoosefi; Sashank J. Reddi; Sanjiv Kumar (CoRR, 2024)

Efficient Document Ranking with Learnable Late Interactions. - Ziwei Ji; Himanshu Jain; Andreas Veit; Sashank J. Reddi; Sadeep Jayasumana; Ankit Singh Rawat; Aditya Krishna Menon; Felix X. Yu; Sanjiv Kumar (CoRR, 2024)

Mimetic Initialization Helps State Space Models Learn to Recall. - Asher Trockman; Hrayr Harutyunyan; J. Zico Kolter; Sanjiv Kumar; Srinadh Bhojanapalli (CoRR, 2024)

No more hard prompts: SoftSRV prompting for synthetic data generation. - Giulia DeSalvo; Jean-François Kagy; Lazaros Karydas; Afshin Rostamizadeh; Sanjiv Kumar (CoRR, 2024)

A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. - Ankit Singh Rawat; Veeranjaneyulu Sadhanala; Afshin Rostamizadeh; Ayan Chakrabarti; Wittawat Jitkrittum; Vladimir Feinberg; Seungyeon Kim 001; Hrayr Harutyunyan; Nikunj Saunshi; Zachary Nado; Rakesh Shivanna; Sashank J. Reddi; et al. (CoRR, 2024)

On the Role of Depth and Looping for In-Context Learning with Task Diversity. - Khashayar Gatmiry; Nikunj Saunshi; Sashank J. Reddi; Stefanie Jegelka; Sanjiv Kumar (CoRR, 2024)

LAuReL: Learned Augmented Residual Layer. - Gaurav Menghani; Ravi Kumar; Sanjiv Kumar (CoRR, 2024)

LatentCRF: Continuous CRF for Efficient Latent Diffusion. - Kanchana Ranasinghe; Sadeep Jayasumana; Andreas Veit; Ayan Chakrabarti; Daniel Glasner; Michael S. Ryoo; Srikumar Ramalingam; Sanjiv Kumar (CoRR, 2024)

What do larger image classifiers memorise? - Michal Lukasik; Vaishnavh Nagarajan; Ankit Singh Rawat; Aditya Krishna Menon; Sanjiv Kumar (Trans. Mach. Learn. Res., 2024)

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 (ICLR, 2025)

Faster Cascades via Speculative Decoding. - Harikrishna Narasimhan; Wittawat Jitkrittum; Ankit Singh Rawat; Seungyeon Kim 001; Neha Gupta; Aditya Krishna Menon; 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)

Reasoning with Latent Thoughts: On the Power of Looped Transformers. - Nikunj Saunshi; Nishanth Dikkala; Zhiyuan Li 005; Sanjiv Kumar; Sashank J. Reddi (ICLR, 2025)

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

Analyzing Similarity Metrics for Data Selection for Language Model Pretraining. - Dylan Sam; Ayan Chakrabarti; Afshin Rostamizadeh; Srikumar Ramalingam; Gui Citovsky; Sanjiv Kumar (CoRR, 2025)

Universal Model Routing for Efficient LLM Inference. - Wittawat Jitkrittum; Harikrishna Narasimhan; Ankit Singh Rawat; Jeevesh Juneja; Zifeng Wang; Chen-Yu Lee; Pradeep Shenoy; Rina Panigrahy; Aditya Krishna Menon; Sanjiv Kumar (CoRR, 2025)

Structured Preconditioners in Adaptive Optimization: A Unified Analysis. - Shuo Xie; Tianhao Wang; Sashank Reddi; Sanjiv Kumar; Zhiyuan Li (CoRR, 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 (CoRR, 2025)

Spark Transformer: Reactivating Sparsity in 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; et al. (CoRR, 2025)