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ORCIDSujay Sanghavi
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- affiliation: University of Texas at Austin, USA
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2020 – today
- 2024
[c78]Pedram Akbarian, Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho:
Improving Computational Complexity in Statistical Models with Local Curvature Information. ICML 2024- [c77]Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi:
Understanding the Training Speedup from Sampling with Approximate Losses. ICML 2024 - [c76]Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep P. Chinchali:
Time Weaver: A Conditional Time Series Generation Model. ICML 2024 - [i83]Anish Acharya, Sujay Sanghavi:
Contrastive Approach to Prior Free Positive Unlabeled Learning. CoRR abs/2402.06038 (2024) - [i82]Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi:
Understanding the Training Speedup from Sampling with Approximate Losses. CoRR abs/2402.07052 (2024) - [i81]Rudrajit Das, Naman Agarwal, Sujay Sanghavi, Inderjit S. Dhillon:
Towards Quantifying the Preconditioning Effect of Adam. CoRR abs/2402.07114 (2024) - [i80]Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, Sanjay Shakkottai:
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. CoRR abs/2402.11639 (2024) - [i79]Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali:
Time Weaver: A Conditional Time Series Generation Model. CoRR abs/2403.02682 (2024) - [i78]Sunny Sanyal, Sujay Sanghavi, Alexandros G. Dimakis:
Pre-training Small Base LMs with Fewer Tokens. CoRR abs/2404.08634 (2024) - [i77]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 abs/2405.19597 (2024) - [i76]Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi, Aryan Mokhtari:
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. CoRR abs/2406.02016 (2024) - [i75]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 abs/2406.11206 (2024) - [i74]Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar 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 abs/2406.11794 (2024) - [i73]Anish Acharya, Inderjit S. Dhillon, Sujay Sanghavi:
Geometric Median (GM) Matching for Robust Data Pruning. CoRR abs/2406.17188 (2024) - 2023
- [c75]Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei:
Sample Efficiency of Data Augmentation Consistency Regularization. AISTATS 2023: 3825-3853 - [c74]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. ICLR 2023 - [c73]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. ICML 2023: 7066-7101 - [c72]Rudrajit Das, Sujay Sanghavi:
Understanding Self-Distillation in the Presence of Label Noise. ICML 2023: 7102-7140 - [c71]Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi:
Logarithmic Bayes Regret Bounds. NeurIPS 2023 - [i72]Rudrajit Das, Sujay Sanghavi:
Understanding Self-Distillation in the Presence of Label Noise. CoRR abs/2301.13304 (2023) - [i71]Sunny Sanyal, Jean Kaddour, Abhishek Kumar, Sujay Sanghavi:
Understanding the Effectiveness of Early Weight Averaging for Training Large Language Models. CoRR abs/2306.03241 (2023) - [i70]Alexia Atsidakou, Branislav Kveton, Sumeet Katariya, Constantine Caramanis, Sujay Sanghavi:
Logarithmic Bayes Regret Bounds. CoRR abs/2306.09136 (2023) - [i69]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 abs/2308.00177 (2023) - 2022
- [j27]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): 2727-2739 (2022) - [c70]Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho:
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. AISTATS 2022: 3930-3961 - [c69]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: 11145-11168 - [c68]Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai:
Asymptotically-Optimal Gaussian Bandits with Side Observations. ICML 2022: 1057-1077 - [c67]Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi:
Linear Bandit Algorithms with Sublinear Time Complexity. ICML 2022: 25241-25260 - [c66]Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant:
Minimax Regret for Cascading Bandits. NeurIPS 2022 - [c65]Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan:
Toward Understanding Privileged Features Distillation in Learning-to-Rank. NeurIPS 2022 - [c64]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: 496-506 - [i68]Tongzheng Ren, Jiacheng Zhuo, Sujay Sanghavi, Nhat Ho:
Improving Computational Complexity in Statistical Models with Second-Order Information. CoRR abs/2202.04219 (2022) - [i67]Shuo Yang, Yijun Dong, Rachel A. Ward, Inderjit S. Dhillon, Sujay Sanghavi, Qi Lei:
Sample Efficiency of Data Augmentation Consistency Regularization. CoRR abs/2202.12230 (2022) - [i66]Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant:
Minimax Regret for Cascading Bandits. CoRR abs/2203.12577 (2022) - [i65]Nhat Ho, Tongzheng Ren, Sujay Sanghavi, Purnamrita Sarkar, Rachel A. Ward:
An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models. CoRR abs/2205.07999 (2022) - [i64]Tongzheng Ren, Fuheng Cui, Sujay Sanghavi, Nhat Ho:
Beyond EM Algorithm on Over-specified Two-Component Location-Scale Gaussian Mixtures. CoRR abs/2205.11078 (2022) - [i63]Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael G. Rabbat, Inderjit S. Dhillon:
Positive Unlabeled Contrastive Learning. CoRR abs/2206.01206 (2022) - [i62]Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi:
Beyond Uniform Lipschitz Condition in Differentially Private Optimization. CoRR abs/2206.10713 (2022) - [i61]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 abs/2208.05663 (2022) - [i60]Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan:
Toward Understanding Privileged Features Distillation in Learning-to-Rank. CoRR abs/2209.08754 (2022) - [i59]Alexia Atsidakou, Sumeet Katariya, Sujay Sanghavi, Branislav Kveton:
Bayesian Fixed-Budget Best-Arm Identification. CoRR abs/2211.08572 (2022) - [i58]Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai:
Latent Variable Representation for Reinforcement Learning. CoRR abs/2212.08765 (2022) - 2021
- [c63]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: 3717-3726 - [c62]Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi:
Nearly Horizon-Free Offline Reinforcement Learning. NeurIPS 2021: 15621-15634 - [i57]Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi:
Linear Bandit Algorithms with Sublinear Time Complexity. CoRR abs/2103.02729 (2021) - [i56]Shuo Yang, Tongzheng Ren, Inderjit S. Dhillon, Sujay Sanghavi:
Combinatorial Bandits without Total Order for Arms. CoRR abs/2103.02741 (2021) - [i55]Tongzheng Ren, Jialian Li, Bo Dai, Simon S. Du, Sujay Sanghavi:
Nearly Horizon-Free Offline Reinforcement Learning. CoRR abs/2103.14077 (2021) - [i54]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 abs/2106.00730 (2021) - [i53]Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon:
DP-NormFedAvg: Normalizing Client Updates for Privacy-Preserving Federated Learning. CoRR abs/2106.07094 (2021) - [i52]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 abs/2106.08882 (2021) - [i51]Tongzheng Ren, Fuheng Cui, Alexia Atsidakou, Sujay Sanghavi, Nhat Ho:
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent. CoRR abs/2110.07810 (2021) - 2020
- [c61]Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi:
Choosing the Sample with Lowest Loss makes SGD Robust. AISTATS 2020: 2120-2130 - [c60]Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon:
Extreme Multi-label Classification from Aggregated Labels. ICML 2020: 8752-8762 - [i50]Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi:
Choosing the Sample with Lowest Loss makes SGD Robust. CoRR abs/2001.03316 (2020) - [i49]Yanyao Shen, Hsiang-Fu Yu, Sujay Sanghavi, Inderjit S. Dhillon:
Extreme Multi-label Classification from Aggregated Labels. CoRR abs/2004.00198 (2020) - [i48]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 abs/2011.10643 (2020) - [i47]Vatsal Shah, Soumya Basu, Anastasios Kyrillidis, Sujay Sanghavi:
On Generalization of Adaptive Methods for Over-parameterized Linear Regression. CoRR abs/2011.14066 (2020) - [i46]Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon:
Improved Convergence Rates for Non-Convex Federated Learning with Compression. CoRR abs/2012.04061 (2020)
2010 – 2019
- 2019
- [c59]Yanyao Shen, Sujay Sanghavi:
Learning with Bad Training Data via Iterative Trimmed Loss Minimization. ICML 2019: 5739-5748 - [c58]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 - [c57]Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai:
Blocking Bandits. NeurIPS 2019: 4785-4794 - [c56]Yanyao Shen, Sujay Sanghavi:
Iterative Least Trimmed Squares for Mixed Linear Regression. NeurIPS 2019: 6076-6086 - [c55]Shuo Yang, Yanyao Shen, Sujay Sanghavi:
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. NeurIPS 2019: 7924-7934 - [c54]Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis:
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. NeurIPS 2019: 8069-8079 - [c53]Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi:
Learning Distributions Generated by One-Layer ReLU Networks. NeurIPS 2019: 8105-8115 - [c52]Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Sujay Sanghavi, Mattan Erez:
PruneTrain: fast neural network training by dynamic sparse model reconfiguration. SC 2019: 36:1-36:13 - [i45]Yanyao Shen, Sujay Sanghavi:
Iterative Least Trimmed Squares for Mixed Linear Regression. CoRR abs/1902.03653 (2019) - [i44]Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai:
Blocking Bandits. CoRR abs/1907.11975 (2019) - [i43]Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi:
Learning Distributions Generated by One-Layer ReLU Networks. CoRR abs/1909.01812 (2019) - [i42]Shuo Yang, Yanyao Shen, Sujay Sanghavi:
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. CoRR abs/1911.03034 (2019) - 2018
- [j26]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): 2165-2204 (2018) - [j25]Avik Ray, Sujay Sanghavi, Sanjay Shakkottai:
Searching for a Single Community in a Graph. ACM Trans. Model. Perform. Evaluation Comput. Syst. 3(3): 13:1-13:17 (2018) - [i41]Avik Ray, Sujay Sanghavi, Sanjay Shakkottai:
Searching for a Single Community in a Graph. CoRR abs/1806.07944 (2018) - [i40]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) - [i39]Yanyao Shen, Sujay Sanghavi:
Iteratively Learning from the Best. CoRR abs/1810.11874 (2018) - [i38]Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis:
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. CoRR abs/1810.11905 (2018) - 2017
- [j24]Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai:
The Search Problem in Mixture Models. J. Mach. Learn. Res. 18: 206:1-206:61 (2017) - [c51]Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi:
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach. AISTATS 2017: 65-74 - [i37]Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sujay Sanghavi:
Sparse Quadratic Logistic Regression in Sub-quadratic Time. CoRR abs/1703.02682 (2017) - [i36]Anastasios Kyrillidis, Amir Kalev, Dohyung Park, Srinadh Bhojanapalli, Constantine Caramanis, Sujay Sanghavi:
Provable quantum state tomography via non-convex methods. CoRR abs/1711.02524 (2017) - 2016
- [j23]Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai:
Online Collaborative Filtering on Graphs. Oper. Res. 64(3): 756-769 (2016) - [j22]Changxiao Cai, Sujay Sanghavi, Haris Vikalo:
Structured Low-Rank Matrix Factorization for Haplotype Assembly. IEEE J. Sel. Top. Signal Process. 10(4): 647-657 (2016) - [j21]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): 503-526 (2016) - [j20]Sharayu Moharir, Sujay Sanghavi, Sanjay Shakkottai:
Online Load Balancing Under Graph Constraints. IEEE/ACM Trans. Netw. 24(3): 1690-1703 (2016) - [c50]Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi:
Finding low-rank solutions to smooth convex problems via the Burer-Monteiro approach. Allerton 2016: 439-446 - [c49]Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi:
Dropping Convexity for Faster Semi-definite Optimization. COLT 2016: 530-582 - [c48]Changxiao Cai, Sujay Sanghavi, Haris Vikalo:
Structurally-constrained gradient descent for matrix factorization in haplotype assembly problems. ICASSP 2016: 2638-2641 - [c47]Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis:
Single Pass PCA of Matrix Products. NIPS 2016: 2577-2585 - [c46]Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi:
Normalized Spectral Map Synchronization. NIPS 2016: 4925-4933 - [c45]Avik Ray, Sujay Sanghavi, Sanjay Shakkottai:
Searching For A Single Community in a Graph. SIGMETRICS 2016: 399-400 - [i35]Vatsal Shah, Megasthenis Asteris, Anastasios Kyrillidis, Sujay Sanghavi:
Trading-off variance and complexity in stochastic gradient descent. CoRR abs/1603.06861 (2016) - [i34]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 abs/1606.01316 (2016) - [i33]Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi:
Finding Low-rank Solutions to Matrix Problems, Efficiently and Provably. CoRR abs/1606.03168 (2016) - [i32]Xinyang Yi, Constantine Caramanis, Sujay Sanghavi:
Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization. CoRR abs/1608.05749 (2016) - [i31]Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi:
Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach. CoRR abs/1609.03240 (2016) - [i30]Avik Ray, Joe Neeman, Sujay Sanghavi, Sanjay Shakkottai:
The Search Problem in Mixture Models. CoRR abs/1610.00843 (2016) - [i29]Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis:
Single Pass PCA of Matrix Products. CoRR abs/1610.06656 (2016) - 2015
- [j19]Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward:
Completing any low-rank matrix, provably. J. Mach. Learn. Res. 16: 2999-3034 (2015) - [j18]Sharayu Moharir, Javad Ghaderi, Sujay Sanghavi, Sanjay Shakkottai:
Serving content with unknown demand: the high-dimensional regime. Queueing Syst. Theory Appl. 81(2-3): 231-264 (2015) - [j17]Avik Ray, Sujay Sanghavi, Sanjay Shakkottai:
Improved Greedy Algorithms for Learning Graphical Models. IEEE Trans. Inf. Theory 61(6): 3457-3468 (2015) - [j16]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval Using Alternating Minimization. IEEE Trans. Signal Process. 63(18): 4814-4826 (2015) - [c44]Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit S. Dhillon:
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons. ICML 2015: 1907-1916 - [c43]Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. NIPS 2015: 1090-1098 - [c42]Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi:
Tighter Low-rank Approximation via Sampling the Leveraged Element. SODA 2015: 902-920 - [i28]Srinadh Bhojanapalli, Sujay Sanghavi:
A New Sampling Technique for Tensors. CoRR abs/1502.05023 (2015) - [i27]Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. CoRR abs/1506.02348 (2015) - [i26]Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit S. Dhillon:
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons. CoRR abs/1507.04457 (2015) - [i25]Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi:
Dropping Convexity for Faster Semi-definite Optimization. CoRR abs/1509.03917 (2015) - 2014
- [j15]Yudong Chen, Ali Jalali, Sujay Sanghavi, Huan Xu:
Clustering partially observed graphs via convex optimization. J. Mach. Learn. Res. 15(1): 2213-2238 (2014) - [j14]Yudong Chen, Sujay Sanghavi, Huan Xu:
Improved Graph Clustering. IEEE Trans. Inf. Theory 60(10): 6440-6455 (2014) - [c41]Avik Ray, Javad Ghaderi, Sujay Sanghavi, Sanjay Shakkottai:
Overlap graph clustering via successive removal. Allerton 2014: 278-285 - [c40]Xinyang Yi, Constantine Caramanis, Sujay Sanghavi:
Alternating Minimization for Mixed Linear Regression. ICML 2014: 613-621 - [c39]Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel A. Ward:
Coherent Matrix Completion. ICML 2014: 674-682 - [c38]Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath:
Learning structure of power-law Markov networks. ISIT 2014: 2272-2276 - [c37]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. NIPS 2014: 1107-1115 - [c36]Dohyung Park, Constantine Caramanis, Sujay Sanghavi:
Greedy Subspace Clustering. NIPS 2014: 2753-2761 - [c35]Sharayu Moharir, Javad Ghaderi, Sujay Sanghavi, Sanjay Shakkottai:
Serving content with unknown demand: the high-dimensional regime. SIGMETRICS 2014: 435-447 - [c34]Avik Ray, Sujay Sanghavi, Sanjay Shakkottai:
Topic modeling from network spread. SIGMETRICS 2014: 561-562 - [e1]Sujay Sanghavi, Sanjay Shakkottai, Marc Lelarge, Bianca Schroeder:
ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2014, Austin, TX, USA, June 16-20, 2014. ACM 2014, ISBN 978-1-4503-2789-3 [contents] - [i24]Srinadh Bhojanapalli, Prateek Jain, Sujay Sanghavi:
Tighter Low-rank Approximation via Sampling the Leveraged Element. CoRR abs/1410.3886 (2014) - [i23]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. CoRR abs/1410.7660 (2014) - [i22]Dohyung Park, Constantine Caramanis, Sujay Sanghavi:
Greedy Subspace Clustering. CoRR abs/1410.8864 (2014) - [i21]Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai:
Online Collaborative-Filtering on Graphs. CoRR abs/1411.2057 (2014) - [i20]Sharayu Moharir, Javad Ghaderi, Sujay Sanghavi, Sanjay Shakkottai:
Serving Content with Unknown Demand: the High-Dimensional Regime. CoRR abs/1412.6463 (2014) - 2013
- [j13]Yudong Chen, Ali Jalali, Sujay Sanghavi, Constantine Caramanis:
Low-Rank Matrix Recovery From Errors and Erasures. IEEE Trans. Inf. Theory 59(7): 4324-4337 (2013) - [j12]Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi:
A Dirty Model for Multiple Sparse Regression. IEEE Trans. Inf. Theory 59(12): 7947-7968 (2013) - [c33]Avhishek Chatterjee, Ankit Singh Rawat, Sriram Vishwanath, Sujay Sanghavi:
Learning the causal graph of Markov time series. Allerton 2013: 107-114 - [c32]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval using Alternating Minimization. NIPS 2013: 2796-2804 - [c31]Sharayu Moharir, Sujay Sanghavi, Sanjay Shakkottai:
Online load balancing under graph constraints. SIGMETRICS 2013: 363-364 - [c30]Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi:
Low-rank matrix completion using alternating minimization. STOC 2013: 665-674 - [i19]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval using Alternating Minimization. CoRR abs/1306.0160 (2013) - [i18]Srinadh Bhojanapalli, Yudong Chen, Sujay Sanghavi, Rachel A. Ward:
Coherent Matrix Completion. CoRR abs/1306.2979 (2013) - 2012
- [j11]Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust PCA via Outlier Pursuit. IEEE Trans. Inf. Theory 58(5): 3047-3064 (2012) - [c29]Sharayu Moharir, Sujay Sanghavi:
Online load balancing and correlated randomness. Allerton Conference 2012: 746-753 - [c28]Avik Ray, Sujay Sanghavi, Sanjay Shakkottai:
Greedy learning of graphical models with small girth. Allerton Conference 2012: 2024-2031 - [c27]Ali Jalali, Sujay Sanghavi:
Learning the Dependence Graph of Time Series with Latent Factors. ICML 2012 - [c26]Aneesh Reddy, Sujay Sanghavi, Sanjay Shakkottai:
On the effect of channel fading on greedy scheduling. INFOCOM 2012: 406-414 - [c25]Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath:
Learning Markov graphs up to edit distance. ISIT 2012: 2731-2735 - [c24]Yudong Chen, Sujay Sanghavi, Huan Xu:
Clustering Sparse Graphs. NIPS 2012: 2213-2221 - [c23]Praneeth Netrapalli, Sujay Sanghavi:
Learning the graph of epidemic cascades. SIGMETRICS 2012: 211-222 - [c22]Ali Jalali, Sujay Sanghavi:
Greedy dirty models: A new algorithm for multiple sparse regression. SSP 2012: 416-419 - [i17]Praneeth Netrapalli, Sujay Sanghavi:
Finding the Graph of Epidemic Cascades. CoRR abs/1202.1779 (2012) - [i16]Aneesh Reddy, Sujay Sanghavi, Sanjay Shakkottai:
On the Effect of Channel Fading on Greedy Scheduling. CoRR abs/1203.1997 (2012) - [i15]Ali Jalali, Sujay Sanghavi:
A New Greedy Algorithm for Multiple Sparse Regression. CoRR abs/1206.1402 (2012) - [i14]Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi:
Low-rank Matrix Completion using Alternating Minimization. CoRR abs/1212.0467 (2012) - 2011
- [j10]Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Parrilo, Alan S. Willsky:
Rank-Sparsity Incoherence for Matrix Decomposition. SIAM J. Optim. 21(2): 572-596 (2011) - [j9]Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Willsky:
Belief Propagation and LP Relaxation for Weighted Matching in General Graphs. IEEE Trans. Inf. Theory 57(4): 2203-2212 (2011) - [c21]Yudong Chen, Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust Matrix Completion and Corrupted Columns. ICML 2011: 873-880 - [c20]Ali Jalali, Yudong Chen, Sujay Sanghavi, Huan Xu:
Clustering Partially Observed Graphs via Convex Optimization. ICML 2011: 1001-1008 - [c19]Yudong Chen, Ali Jalali, Sujay Sanghavi, Constantine Caramanis:
Low-rank matrix recovery from errors and erasures. ISIT 2011: 2313-2317 - [c18]Ali Jalali, Pradeep Ravikumar, Vishvas Vasuki, Sujay Sanghavi:
On Learning Discrete Graphical Models using Group-Sparse Regularization. AISTATS 2011: 378-387 - [i13]Yudong Chen, Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust Matrix Completion with Corrupted Columns. CoRR abs/1102.2254 (2011) - [i12]Yudong Chen, Ali Jalali, Sujay Sanghavi, Constantine Caramanis:
Low-rank Matrix Recovery from Errors and Erasures. CoRR abs/1104.0354 (2011) - [i11]Ali Jalali, Yudong Chen, Sujay Sanghavi, Huan Xu:
Clustering Partially Observed Graphs via Convex Optimization. CoRR abs/1104.4803 (2011) - [i10]Ali Jalali, Sujay Sanghavi:
Learning with Latent Factors in Time Series. CoRR abs/1106.1887 (2011) - [i9]Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi:
A Dirty Model for Multiple Sparse Regression. CoRR abs/1106.5826 (2011) - 2010
- [j8]Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Willsky:
Sequential Compressed Sensing. IEEE J. Sel. Top. Signal Process. 4(2): 435-444 (2010) - [j7]Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher III, Alan S. Willsky:
Learning graphical models for hypothesis testing and classification. IEEE Trans. Signal Process. 58(11): 5481-5495 (2010) - [c17]Praneeth Netrapalli, Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai:
Greedy learning of Markov network structure. Allerton 2010: 1295-1302 - [c16]Yuxin Chen, Sujay Sanghavi:
A general framework for high-dimensional estimation in the presence of incoherence. Allerton 2010: 1570-1576 - [c15]Ali Jalali, Pradeep Ravikumar, Sujay Sanghavi, Chao Ruan:
A Dirty Model for Multi-task Learning. NIPS 2010: 964-972 - [c14]Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust PCA via Outlier Pursuit. NIPS 2010: 2496-2504 - [i8]Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Willsky:
Sequential Compressed Sensing. CoRR abs/1003.0219 (2010) - [i7]Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust PCA via Outlier Pursuit. CoRR abs/1010.4237 (2010)
2000 – 2009
- 2009
- [j6]Gagan Raj Gupta, Sujay Sanghavi, Ness B. Shroff:
Workload optimality in switches without arrivals. SIGMETRICS Perform. Evaluation Rev. 37(2): 36-38 (2009) - [j5]Sujay Sanghavi, Devavrat Shah, Alan S. Willsky:
Message passing for maximum weight independent set. IEEE Trans. Inf. Theory 55(11): 4822-4834 (2009) - [j4]Loc Bui, Sujay Sanghavi, R. Srikant:
Distributed link scheduling with constant overhead. IEEE/ACM Trans. Netw. 17(5): 1467-1480 (2009) - [c13]Gagan Raj Gupta, Sujay Sanghavi, Ness B. Shroff:
Is it enough to drain the heaviest bottlenecks? Allerton 2009: 483-490 - [c12]Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Parrilo, Alan S. Willsky:
Sparse and low-rank matrix decompositions. Allerton 2009: 962-967 - [c11]Ali Jalali, David James Love, Sujay Sanghavi, Bertrand M. Hochwald:
Conferencing on trees. CISS 2009: 763-768 - [c10]Shashi Borade, Sujay Sanghavi:
Some fundamental coding theoretic limits of unequal error protection. ISIT 2009: 2231-2235 - [c9]Gagan Raj Gupta, Sujay Sanghavi, Ness B. Shroff:
Node weighted scheduling. SIGMETRICS/Performance 2009: 97-108 - [i6]Gagan Raj Gupta, Sujay Sanghavi, Ness B. Shroff:
Node Weighted Scheduling. CoRR abs/0902.1169 (2009) - 2008
- [j3]Sujay Sanghavi, Bruce E. Hajek:
A New Mechanism for the Free-Rider Problem. IEEE Trans. Autom. Control. 53(5): 1176-1183 (2008) - [c8]Sujay Sanghavi, Dmitry Malioutov, Alan S. Willsky:
Networking sensors using belief propagation. Allerton 2008: 384-391 - [c7]Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Willsky:
Compressed sensing with sequential observations. ICASSP 2008: 3357-3360 - [i5]Sujay Sanghavi, Devavrat Shah, Alan S. Willsky:
Message-passing for Maximum Weight Independent Set. CoRR abs/0807.5091 (2008) - 2007
- [j2]Sujay Sanghavi, Bruce E. Hajek, Laurent Massoulié:
Gossiping With Multiple Messages. IEEE Trans. Inf. Theory 53(12): 4640-4654 (2007) - [c6]Sujay Sanghavi, Bruce E. Hajek, Laurent Massoulié:
Gossiping with Multiple Messages. INFOCOM 2007: 2135-2143 - [c5]Sujay Sanghavi, Dmitry M. Malioutov, Alan S. Willsky:
Linear programming analysis of loopy belief propagation for weighted matching. NIPS 2007: 1273-1280 - [c4]Sujay Sanghavi, Devavrat Shah, Alan S. Willsky:
Message Passing for Max-weight Independent Set. NIPS 2007: 1281-1288 - [c3]Sujay Sanghavi, Loc Bui, R. Srikant:
Distributed link scheduling with constant overhead. SIGMETRICS 2007: 313-324 - [i4]Sujay Sanghavi:
Equivalence of LP Relaxation and Max-Product for Weighted Matching in General Graphs. CoRR abs/0705.0760 (2007) - 2006
- [b1]Sujay Sanghavi:
Decentralized Network Algorithms. University of Illinois Urbana-Champaign, USA, 2006 - [j1]Sujay Sanghavi, Bruce E. Hajek:
Adaptive induced fluctuations for multiuser diversity. IEEE Trans. Wirel. Commun. 5(6): 1294-1305 (2006) - [i3]Sujay Sanghavi, Loc Bui, R. Srikant:
Distributed Link Scheduling with Constant Overhead. CoRR abs/cs/0611064 (2006) - [i2]Sujay Sanghavi:
Intermediate Performance of Rateless Codes. CoRR abs/cs/0612075 (2006) - [i1]Sujay Sanghavi, Bruce E. Hajek, Laurent Massoulié:
Gossiping with Multiple Messages. CoRR abs/cs/0612118 (2006) - 2005
- [c2]Sujay Sanghavi, Bruce E. Hajek:
A new mechanism for the free-rider problem. P2PECON@SIGCOMM 2005: 122-127 - 2004
- [c1]Sujay Sanghavi, Bruce E. Hajek:
Optimal allocation of a divisible good to strategic buyers. CDC 2004: 2748-2753
Coauthor Index
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