Sujay Sanghavi

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 94

Top Venues

CoRR

ICML

NeurIPS

NIPS

Publications

Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali
Time Weaver: A Conditional Time Series Generation Model. CoRR (2024)
Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi
Understanding the Training Speedup from Sampling with Approximate Losses. CoRR (2024)
Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi, Aryan Mokhtari
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. CoRR (2024)
Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar
DataComp-LM: In search of the next generation of training sets for language models. CoRR (2024)
Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi
SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors. CoRR (2024)
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong
Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR (2024)
Anish Acharya, Sujay Sanghavi
Contrastive Approach to Prior Free Positive Unlabeled Learning. CoRR (2024)
Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, Sanjay Shakkottai
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. CoRR (2024)
Anish Acharya, Inderjit S. Dhillon, Sujay Sanghavi
Geometric Median (GM) Matching for Robust Data Pruning. CoRR (2024)
Rudrajit Das, Naman Agarwal, Sujay Sanghavi, Inderjit S. Dhillon
Towards Quantifying the Preconditioning Effect of Adam. CoRR (2024)
Sunny Sanyal, Sujay Sanghavi, Alexandros G. Dimakis
Pre-training Small Base LMs with Fewer Tokens. CoRR (2024)
Rudrajit Das, Sujay Sanghavi
Understanding Self-Distillation in the Presence of Label Noise. ICML (2023)
Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi
Logarithmic Bayes Regret Bounds. CoRR (2023)
Shuo Yang, Yijun Dong, Rachel Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei
Sample Efficiency of Data Augmentation Consistency Regularization. AISTATS (2023)
Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky, Giulia Fanti, Yesh Dattatreya, Sujay Sanghavi
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity. CoRR (2023)
Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai
Latent Variable Representation for Reinforcement Learning. ICLR (2023)
Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi
Logarithmic Bayes Regret Bounds. NeurIPS (2023)
Rudrajit Das, Sujay Sanghavi
Understanding Self-Distillation in the Presence of Label Noise. CoRR (2023)
Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML (2023)
Sunny Sanyal, Jean Kaddour, Abhishek Kumar, Sujay Sanghavi
Understanding the Effectiveness of Early Weight Averaging for Training Large Language Models. CoRR (2023)
Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. AISTATS (2022)
Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo, Sujay Sanghavi, Inderjit S. Dhillon
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. IEEE Trans. Parallel Distributed Syst. 33 (11) (2022)
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan
Toward Understanding Privileged Features Distillation in Learning-to-Rank. NeurIPS (2022)
Alexia Atsidakou, Sumeet Katariya, Sujay Sanghavi, Branislav Kveton
Bayesian Fixed-Budget Best-Arm Identification. CoRR (2022)
Tongzheng Ren, Fuheng Cui, Sujay Sanghavi, Nhat Ho
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures. CoRR (2022)
Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant
Minimax Regret for Cascading Bandits. NeurIPS (2022)
Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai
Asymptotically-Optimal Gaussian Bandits with Side Observations. ICML (2022)
Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. AISTATS (2022)
Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho
Improving Computational Complexity in Statistical Models with Second-Order Information. CoRR (2022)
Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR (2022)
Nan Jiang, Dhivya Eswaran, Choon Hui Teo, Yexiang Xue, Yesh Dattatreya, Sujay Sanghavi, Vishy Vishwanathan
On the Value of Behavioral Representations for Dense Retrieval. CoRR (2022)
Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan
Toward Understanding Privileged Features Distillation in Learning-to-Rank. CoRR (2022)
Shuo Yang, Yijun Dong, Rachel Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei
Sample Efficiency of Data Augmentation Consistency Regularization. CoRR (2022)
Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai
Latent Variable Representation for Reinforcement Learning. CoRR (2022)
Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael Rabbat, Inderjit S. Dhillon
Positive Unlabeled Contrastive Learning. CoRR (2022)
Nhat Ho, Tongzheng Ren, Sujay Sanghavi, Purnamrita Sarkar, Rachel Ward
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models. CoRR (2022)
Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant
Minimax Regret for Cascading Bandits. CoRR (2022)
Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi
Linear Bandit Algorithms with Sublinear Time Complexity. ICML (2022)
Rudrajit Das, Anish Acharya, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu
Faster non-convex federated learning via global and local momentum. UAI (2022)
Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, Inderjit S. Dhillon
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification. CoRR (2021)
Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent. CoRR (2021)
Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi
Nearly Horizon-Free Offline Reinforcement Learning. CoRR (2021)
Tavor Z. Baharav, Daniel L. Jiang, Kedarnath Kolluri, Sujay Sanghavi, Inderjit S. Dhillon
Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification. CIKM (2021)
Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi
Linear Bandit Algorithms with Sublinear Time Complexity. CoRR (2021)
Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon, Sujay Sanghavi
Combinatorial Bandits without Total Order for Arms. CoRR (2021)
Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon
DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning. CoRR (2021)
Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. CoRR (2021)
Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi
Nearly Horizon-Free Offline Reinforcement Learning. NeurIPS (2021)
Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi
Choosing the Sample with Lowest Loss makes SGD Robust. AISTATS (2020)
Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon
Improved Convergence Rates for Non-Convex Federated Learning with Compression. CoRR (2020)
Vatsal Shah, Soumya Basu, Anastasios Kyrillidis, Sujay Sanghavi
On Generalization of Adaptive Methods for Over-parameterized Linear Regression. CoRR (2020)
Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon
Extreme Multi-label Classification from Aggregated Labels. ICML (2020)
Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi
Choosing the Sample with Lowest Loss makes SGD Robust. CoRR (2020)
Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo, Sujay Sanghavi, Inderjit S. Dhillon
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Optimization. CoRR (2020)
Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon
Extreme Multi-label Classification from Aggregated Labels. CoRR (2020)
Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Sujay Sanghavi, Mattan Erez
PruneTrain: fast neural network training by dynamic sparse model reconfiguration. SC (2019)
Yanyao Shen, Sujay Sanghavi
Learning with Bad Training Data via Iterative Trimmed Loss Minimization. ICML (2019)
Yanyao Shen, Sujay Sanghavi
Iterative Least Trimmed Squares for Mixed Linear Regression. CoRR (2019)
Shuo Yang, Yanyao Shen, Sujay Sanghavi
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. CoRR (2019)
Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. NeurIPS (2019)
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
Learning Distributions Generated by One-Layer ReLU Networks. NeurIPS (2019)
Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai
Blocking Bandits. CoRR (2019)
Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai
Blocking Bandits. NeurIPS (2019)
Shuo Yang, Yanyao Shen, Sujay Sanghavi
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. NeurIPS (2019)
Yanyao Shen, Sujay Sanghavi
Iterative Least Trimmed Squares for Mixed Linear Regression. NeurIPS (2019)
Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi
Learning Distributions Generated by One-Layer ReLU Networks. CoRR (2019)
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)
Avik Ray, Sujay Sanghavi, Sanjay Shakkottai
Searching for a Single Community in a Graph. CoRR (2018)
Yanyao Shen, Sujay Sanghavi
Iteratively Learning from the Best. CoRR (2018)
Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. CoRR (2018)
Avik Ray, Sujay Sanghavi, Sanjay Shakkottai
Searching for a Single Community in a Graph. ACM Trans. Model. Perform. Evaluation Comput. Syst. 3 (3) (2018)
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 (2018)
Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably. SIAM J. Imaging Sci. 11 (4) (2018)
Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sujay Sanghavi
Sparse Quadratic Logistic Regression in Sub-quadratic Time. CoRR (2017)
Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach. AISTATS (2017)
Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai
The Search Problem in Mixture Models. J. Mach. Learn. Res. 18 (2017)
Anastasios Kyrillidis, Amir Kalev, Dohyung Park, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi
Provable quantum state tomography via non-convex methods. CoRR (2017)
Yudong Chen, Huan Xu, Constantine Caramanis, Sujay Sanghavi
Matrix Completion With Column Manipulation: Near-Optimal Sample-Robustness-Rank Tradeoffs. IEEE Trans. Inf. Theory 62 (1) (2016)
Xinyang Yi, Constantine Caramanis, Sujay Sanghavi
Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization. CoRR (2016)
Vatsal Shah, Megasthenis Asteris, Anastasios Kyrillidis, Sujay Sanghavi
Trading-off variance and complexity in stochastic gradient descent. CoRR (2016)
Changxiao Cai, Sujay Sanghavi, Haris Vikalo
Structured Low-Rank Matrix Factorization for Haplotype Assembly. IEEE J. Sel. Top. Signal Process. 10 (4) (2016)
Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
Finding Low-rank Solutions to Matrix Problems, Efficiently and Provably. CoRR (2016)
Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach. CoRR (2016)
Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis
Single Pass PCA of Matrix Products. CoRR (2016)
Avik Ray, Sujay Sanghavi, Sanjay Shakkottai
Searching For A Single Community in a Graph. SIGMETRICS (2016)
Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi
Dropping Convexity for Faster Semi-definite Optimization. COLT (2016)
Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi
Normalized Spectral Map Synchronization. NIPS (2016)
Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai
Online Collaborative Filtering on Graphs. Oper. Res. 64 (3) (2016)
Changxiao Cai, Sujay Sanghavi, Haris Vikalo
Structurally-constrained gradient descent for matrix factorization in haplotype assembly problems. ICASSP (2016)
Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai
The Search Problem in Mixture Models. CoRR (2016)
Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
Finding low-rank solutions to smooth convex problems via the Burer-Monteiro approach. Allerton (2016)
Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis
Single Pass PCA of Matrix Products. NIPS (2016)
Sharayu Moharir, Sujay Sanghavi, Sanjay Shakkottai
Online Load Balancing Under Graph Constraints. IEEE/ACM Trans. Netw. 24 (3) (2016)
Dohyung Park, Anastasios Kyrillidis, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi
Provable non-convex projected gradient descent for a class of constrained matrix optimization problems. CoRR (2016)
Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward
Completing any low-rank matrix, provably. J. Mach. Learn. Res. 16 (2015)
Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi
Tighter Low-rank Approximation via Sampling the Leveraged Element. SODA (2015)
Avik Ray, Sujay Sanghavi, Sanjay Shakkottai
Improved Greedy Algorithms for Learning Graphical Models. IEEE Trans. Inf. Theory 61 (6) (2015)
Srinadh Bhojanapalli, Sujay Sanghavi
A New Sampling Technique for Tensors. CoRR (2015)
Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
Convergence Rates of Active Learning for Maximum Likelihood Estimation. CoRR (2015)
Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
Phase Retrieval Using Alternating Minimization. IEEE Trans. Signal Process. 63 (18) (2015)

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Publication Activity (10 Years)

Top Topics

Top Venues

Sujay Sanghavi