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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 - [i86]Anish Acharya, Sujay Sanghavi:
Contrastive Approach to Prior Free Positive Unlabeled Learning. CoRR abs/2402.06038 (2024) - [i85]Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal, Sujay Sanghavi:
Understanding the Training Speedup from Sampling with Approximate Losses. CoRR abs/2402.07052 (2024) - [i84]Rudrajit Das, Naman Agarwal, Sujay Sanghavi, Inderjit S. Dhillon:
Towards Quantifying the Preconditioning Effect of Adam. CoRR abs/2402.07114 (2024) - [i83]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) - [i82]Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali:
Time Weaver: A Conditional Time Series Generation Model. CoRR abs/2403.02682 (2024) - [i81]Sunny Sanyal, Sujay Sanghavi, Alexandros G. Dimakis:
Pre-training Small Base LMs with Fewer Tokens. CoRR abs/2404.08634 (2024) - [i80]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) - [i79]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) - [i78]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) - [i77]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 F. 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) - [i76]Anish Acharya, Inderjit S. Dhillon, Sujay Sanghavi:
Geometric Median (GM) Matching for Robust Data Pruning. CoRR abs/2406.17188 (2024) - [i75]Atula Tejaswi, Yoonsang Lee, Sujay Sanghavi, Eunsol Choi:
RARe: Retrieval Augmented Retrieval with In-Context Examples. CoRR abs/2410.20088 (2024) - [i74]Parikshit Bansal, Ali Kavis, Sujay Sanghavi:
Understanding Contrastive Learning via Gaussian Mixture Models. CoRR abs/2411.03517 (2024) - [i73]Quentin Fruytier, Aryan Mokhtari, Sujay Sanghavi:
Learning Mixtures of Experts with EM. CoRR abs/2411.06056 (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]