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ORCIDSujay Sanghavi
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- affiliation: University of Texas at Austin, USA
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2020 – today
- 2023
[i69]Rudrajit Das, Sujay Sanghavi:
Understanding Self-Distillation in the Presence of Label Noise. CoRR abs/2301.13304 (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) - [c65]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 - [c64]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 - [c63]Alexia Atsidakou, Orestis Papadigenopoulos, Constantine Caramanis, Sujay Sanghavi, Sanjay Shakkottai:
Asymptotically-Optimal Gaussian Bandits with Side Observations. ICML 2022: 1057-1077 - [c62]Shuo Yang, Tongzheng Ren, Sanjay Shakkottai, Eric Price, Inderjit S. Dhillon, Sujay Sanghavi:
Linear Bandit Algorithms with Sublinear Time Complexity. ICML 2022: 25241-25260 - [c61]Daniel Vial, Sujay Sanghavi, Sanjay Shakkottai, R. Srikant:
Minimax Regret for Cascading Bandits. NeurIPS 2022 - [c60]Shuo Yang, Sujay Sanghavi, Holakou Rahmanian, Jan Bakus, S. V. N. Vishwanathan:
Toward Understanding Privileged Features Distillation in Learning-to-Rank. NeurIPS 2022 - [c59]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 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 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
- [c58]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 - [c57]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
- [c56]Vatsal Shah, Xiaoxia Wu, Sujay Sanghavi:
Choosing the Sample with Lowest Loss makes SGD Robust. AISTATS 2020: 2120-2130 - [c55]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
- [c54]Yanyao Shen, Sujay Sanghavi:
Learning with Bad Training Data via Iterative Trimmed Loss Minimization. ICML 2019: 5739-5748 - [c53]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 - [c52]Soumya Basu, Rajat Sen, Sujay Sanghavi, Sanjay Shakkottai:
Blocking Bandits. NeurIPS 2019: 4785-4794 - [c51]Yanyao Shen, Sujay Sanghavi:
Iterative Least Trimmed Squares for Mixed Linear Regression. NeurIPS 2019: 6076-6086 - [c50]Shuo Yang, Yanyao Shen, Sujay Sanghavi:
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space. NeurIPS 2019: 7924-7934 - [c49]Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis:
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models. NeurIPS 2019: 8069-8079 - [c48]Shanshan Wu, Alexandros G. Dimakis, Sujay Sanghavi:
Learning Distributions Generated by One-Layer ReLU Networks. NeurIPS 2019: 8105-8115 - [c47]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) - [c46]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) - [c45]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 - [c44]Srinadh Bhojanapalli, Anastasios Kyrillidis, Sujay Sanghavi:
Dropping Convexity for Faster Semi-definite Optimization. COLT 2016: 530-582 - [c43]Changxiao Cai, Sujay Sanghavi, Haris Vikalo:
Structurally-constrained gradient descent for matrix factorization in haplotype assembly problems. ICASSP 2016: 2638-2641 - [c42]Shanshan Wu, Srinadh Bhojanapalli, Sujay Sanghavi, Alexandros G. Dimakis:
Single Pass PCA of Matrix Products. NIPS 2016: 2577-2585 - [c41]Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi:
Normalized Spectral Map Synchronization. NIPS 2016: 4925-4933 - [c40]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 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) - [c39]Dohyung Park, Joe Neeman, Jin Zhang, Sujay Sanghavi, Inderjit S. Dhillon:
Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons. ICML 2015: 1907-1916 - [c38]Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. NIPS 2015: 1090-1098 - [c37]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) - [c36]Avik Ray, Javad Ghaderi, Sujay Sanghavi, Sanjay Shakkottai:
Overlap graph clustering via successive removal. Allerton 2014: 278-285 - [c35]Xinyang Yi, Constantine Caramanis, Sujay Sanghavi:
Alternating Minimization for Mixed Linear Regression. ICML 2014: 613-621 - [c34]Yudong Chen, Srinadh Bhojanapalli, Sujay Sanghavi, Rachel Ward:
Coherent Matrix Completion. ICML 2014: 674-682 - [c33]Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath:
Learning structure of power-law Markov networks. ISIT 2014: 2272-2276 - [c32]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. NIPS 2014: 1107-1115 - [c31]Dohyung Park, Constantine Caramanis, Sujay Sanghavi:
Greedy Subspace Clustering. NIPS 2014: 2753-2761 - [c30]Sharayu Moharir, Javad Ghaderi, Sujay Sanghavi, Sanjay Shakkottai:
Serving content with unknown demand: the high-dimensional regime. SIGMETRICS 2014: 435-447 - [c29]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) - [c28]Avhishek Chatterjee, Ankit Singh Rawat, Sriram Vishwanath, Sujay Sanghavi:
Learning the causal graph of Markov time series. Allerton 2013: 107-114 - [c27]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval using Alternating Minimization. NIPS 2013: 2796-2804 - [c26]Sharayu Moharir, Sujay Sanghavi, Sanjay Shakkottai:
Online load balancing under graph constraints. SIGMETRICS 2013: 363-364 - [c25]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 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) - [c24]Sharayu Moharir, Sujay Sanghavi:
Online load balancing and correlated randomness. Allerton Conference 2012: 746-753 - [c23]Avik Ray, Sujay Sanghavi, Sanjay Shakkottai:
Greedy learning of graphical models with small girth. Allerton Conference 2012: 2024-2031 - [c22]Ali Jalali, Sujay Sanghavi:
Learning the Dependence Graph of Time Series with Latent Factors. ICML 2012 - [c21]Aneesh Reddy, Sujay Sanghavi, Sanjay Shakkottai:
On the effect of channel fading on greedy scheduling. INFOCOM 2012: 406-414 - [c20]Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath:
Learning Markov graphs up to edit distance. ISIT 2012: 2731-2735 - [c19]Yudong Chen, Sujay Sanghavi, Huan Xu:
Clustering Sparse Graphs. NIPS 2012: 2213-2221 - [c18]Praneeth Netrapalli, Sujay Sanghavi:
Learning the graph of epidemic cascades. SIGMETRICS 2012: 211-222 - [c17]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) - [c16]Yudong Chen, Huan Xu, Constantine Caramanis, Sujay Sanghavi:
Robust Matrix Completion and Corrupted Columns. ICML 2011: 873-880 - [c15]Ali Jalali, Yudong Chen, Sujay Sanghavi, Huan Xu:
Clustering Partially Observed Graphs via Convex Optimization. ICML 2011: 1001-1008 - [c14]Yudong Chen, Ali Jalali, Sujay Sanghavi, Constantine Caramanis:
Low-rank matrix recovery from errors and erasures. ISIT 2011: 2313-2317 - [c13]