Praneeth Netrapalli

143 papers A* 46A 3B 4Misc 1Journal 80Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A model of errors in transformers. CoRR Suvrat Raju, Praneeth Netrapalli
2025	J	`jnl`	Compressing Many-Shots in In-Context Learning. CoRR Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta, Yerram Varun, Liqian Peng, Jay Yagnik, Praneeth Netrapalli, Cho-Jui Hsieh, Alec Go, Inderjit S. Dhillon, Aditya Kusupati, Prateek Jain
2025	J	`jnl`	Faster Approx. Top-K: Harnessing the Full Power of Two Stages. CoRR Yashas Samaga, Varun Yerram, Spandana Raj Babbula, Prateek Jain, Praneeth Netrapalli
2025	J	`jnl`	Spark Transformer: Reactivating Sparsity in FFN and Attention. CoRR 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, Alek Andreev, Shreya Pathak, Felix X. Yu, Prateek Jain, David E. Culler, Henry M. Levy, Sanjiv Kumar
2024	—	`conf`	All Mistakes are not Equal: Comprehensive Hierarchy Aware Multilabel Predictions (CHAMP). ICPR (1) Ashwin Vaswani, Yashas Samaga, Gaurav Aggarwal, Praneeth Netrapalli, Narayan G. Hegde
2024	J	`jnl`	Consistent Multiclass Algorithms for Complex Metrics and Constraints. J. Mach. Learn. Res. Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal
2024	J	`jnl`	HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. CoRR Yashas Samaga, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
2024	A*	`conf`	Second Order Methods for Bandit Optimization and Control. COLT Arun Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan
2024	J	`jnl`	Second Order Methods for Bandit Optimization and Control. CoRR Arun Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan
2024	A*	`conf`	Tandem Transformers for Inference Efficient LLMs. ICML Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
2024	J	`jnl`	Tandem Transformers for Inference Efficient LLMs. CoRR Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
2024	A*	`conf`	The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks. NeurIPS Lénaïc Chizat, Praneeth Netrapalli
2023	A*	`conf`	Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks. ICLR Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain
2023	A*	`conf`	Multi-User Reinforcement Learning with Low Rank Rewards. ICML Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain
2023	A*	`conf`	Near Optimal Heteroscedastic Regression with Symbiotic Learning. COLT Aniket Das, Dheeraj M. Nagaraj, Praneeth Netrapalli, Dheeraj Baby
2023	J	`jnl`	Near Optimal Heteroscedastic Regression with Symbiotic Learning. CoRR Dheeraj Baby, Aniket Das, Dheeraj Nagaraj, Praneeth Netrapalli
2023	A*	`conf`	Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. STOC Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
2023	A*	`conf`	Simplicity Bias in 1-Hidden Layer Neural Networks. NeurIPS Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli
2023	J	`jnl`	Simplicity Bias in 1-Hidden Layer Neural Networks. CoRR Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli
2023	J	`jnl`	Steering Deep Feature Learning with Backward Aligned Feature Updates. CoRR Lénaïc Chizat, Praneeth Netrapalli
2022	J	`jnl`	All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP). CoRR Ashwin Vaswani, Gaurav Aggarwal, Praneeth Netrapalli, Narayan G. Hegde
2022	J	`jnl`	Consistent Multiclass Algorithms for Complex Metrics and Constraints. CoRR Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal
2022	J	`jnl`	DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. CoRR Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain
2022	A*	`conf`	Focus on the Common Good: Group Distributional Robustness Follows. ICLR Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
2022	J	`jnl`	Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks. CoRR Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu, Praneeth Netrapalli, Prateek Jain
2022	J	`jnl`	MET: Masked Encoding for Tabular Data. CoRR Kushal Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain
2022	A*	`conf`	Minimax Optimization with Smooth Algorithmic Adversaries. ICLR Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
2022	J	`jnl`	Multi-User Reinforcement Learning with Low Rank Rewards. CoRR Naman Agarwal, Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
2022	A*	`conf`	Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. ICLR Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Mysore Nagaraj, Praneeth Netrapalli
2022	J	`jnl`	Optimistic MLE - A Generic Model-based Algorithm for Partially Observable Sequential Decision Making. CoRR Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
2022	A*	`conf`	Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
2022	J	`jnl`	Reproducibility in Optimization: Theoretical Framework and Limits. CoRR Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
2021	A*	`conf`	Do Input Gradients Highlight Discriminative Features? NeurIPS Harshay Shah, Prateek Jain, Praneeth Netrapalli
2021	J	`jnl`	Do Input Gradients Highlight Discriminative Features? CoRR Harshay Shah, Prateek Jain, Praneeth Netrapalli
2021	A*	`conf`	Efficient Bandit Convex Optimization: Beyond Linear Losses. COLT Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli
2021	J	`jnl`	Focus on the Common Good: Group Distributional Robustness Follows. CoRR Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
2021	J	`jnl`	Making the Last Iterate of SGD Information Theoretically Optimal. SIAM J. Optim. Prateek Jain, Dheeraj M. Nagaraj, Praneeth Netrapalli
2021	J	`jnl`	Minimax Optimization with Smooth Algorithmic Adversaries. CoRR Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
2021	A*	`conf`	Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness. NeurIPS Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
2021	J	`jnl`	Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness. CoRR Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
2021	A*	`conf`	Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. NeurIPS Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
2021	J	`jnl`	Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. CoRR Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
2021	A	`conf`	No Quantum Speedup over Gradient Descent for Non-Smooth Convex Optimization. ITCS Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
2021	J	`jnl`	On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2021	J	`jnl`	Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. CoRR Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
2021	J	`jnl`	Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
2021	A*	`conf`	Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
2021	J	`jnl`	Sample Efficient Linear Meta-Learning by Alternating Minimization. CoRR Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
2021	A*	`conf`	Statistically and Computationally Efficient Linear Meta-representation Learning. NeurIPS Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
2021	A*	`conf`	Streaming Linear System Identification with Reverse Experience Replay. NeurIPS Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
2021	J	`jnl`	Streaming Linear System Identification with Reverse Experience Replay. CoRR Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
2020	A*	`conf`	Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. ICML Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
2020	J	`jnl`	Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. CoRR Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
2020	A*	`conf`	Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. NeurIPS Arun Sai Suggala, Praneeth Netrapalli
2020	J	`jnl`	Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. CoRR Arun Sai Suggala, Praneeth Netrapalli
2020	J	`jnl`	Learning Minimax Estimators via Online Learning. CoRR Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar
2020	A*	`conf`	Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. NeurIPS Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli
2020	J	`jnl`	Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. CoRR Guy Bresler, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Xian Wu
2020	B	`conf`	Leverage Score Sampling for Faster Accelerated Regression and ERM. ALT Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
2020	J	`jnl`	MOReL : Model-Based Offline Reinforcement Learning. CoRR Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
2020	A*	`conf`	MOReL: Model-Based Offline Reinforcement Learning. NeurIPS Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
2020	J	`jnl`	No quantum speedup over gradient descent for non-smooth convex optimization. CoRR Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
2020	B	`conf`	Online Non-Convex Learning: Following the Perturbed Leader is Optimal. ALT Arun Sai Suggala, Praneeth Netrapalli
2020	A*	`conf`	P-SIF: Document Embeddings Using Partition Averaging. AAAI Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar
2020	J	`jnl`	P-SIF: Document Embeddings Using Partition Averaging. CoRR Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar
2020	A*	`conf`	Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. NeurIPS Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
2020	J	`jnl`	Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. CoRR Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
2020	A*	`conf`	The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
2020	J	`jnl`	The Pitfalls of Simplicity Bias in Neural Networks. CoRR Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
2020	A*	`conf`	What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2019	J	`jnl`	A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2019	A*	`conf`	Efficient Algorithms for Smooth Minimax Optimization. NeurIPS Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
2019	J	`jnl`	Efficient Algorithms for Smooth Minimax Optimization. CoRR Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
2019	A*	`conf`	Making the Last Iterate of SGD Information Theoretically Optimal. COLT Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
2019	J	`jnl`	Making the Last Iterate of SGD Information Theoretically Optimal. CoRR Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
2019	J	`jnl`	Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2019	J	`jnl`	Non-Gaussianity of Stochastic Gradient Noise. CoRR Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli
2019	J	`jnl`	Online Non-Convex Learning: Following the Perturbed Leader is Optimal. CoRR Arun Sai Suggala, Praneeth Netrapalli
2019	A*	`conf`	Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli
2019	A*	`conf`	SGD without Replacement: Sharper Rates for General Smooth Convex Functions. ICML Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
2019	J	`jnl`	SGD without Replacement: Sharper Rates for General Smooth Convex Functions. CoRR Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
2019	J	`jnl`	Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
2019	A*	`conf`	The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. NeurIPS Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
2019	J	`jnl`	The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
2018	A*	`conf`	Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. COLT Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2018	A*	`conf`	Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2018	Misc	`conf`	On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
2018	—	`conf`	On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
2018	J	`jnl`	On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
2018	A*	`conf`	Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. COLT Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli
2018	J	`jnl`	Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. CoRR Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli
2018	A	`conf`	Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ITCS Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff
2018	A*	`conf`	Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. NeurIPS Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli
2017	J	`jnl`	A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Trans. Inf. Theory Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
2017	—	`conf`	A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). FSTTCS Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford
2017	J	`jnl`	A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). CoRR Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford
2017	J	`jnl`	Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. CoRR Chi Jin, Praneeth Netrapalli, Michael I. Jordan
2017	J	`jnl`	Accelerating Stochastic Gradient Descent. CoRR Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2017	A	`conf`	Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. AISTATS Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2017	A*	`conf`	How to Escape Saddle Points Efficiently. ICML Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	J	`jnl`	How to Escape Saddle Points Efficiently. CoRR Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
2017	J	`jnl`	Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
2017	J	`jnl`	Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. J. Mach. Learn. Res. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2017	J	`jnl`	Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. CoRR Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff
2017	A*	`conf`	Thresholding Based Outlier Robust PCA. COLT Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
2017	J	`jnl`	Thresholding based Efficient Outlier Robust PCA. CoRR Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
2016	A*	`conf`	Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. ICML Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	J	`jnl`	Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. CoRR Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	A*	`conf`	Faster Eigenvector Computation via Shift-and-Invert Preconditioning. ICML Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
2016	J	`jnl`	Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
2016	A*	`conf`	Information-theoretic thresholds for community detection in sparse networks. COLT Jess Banks, Cristopher Moore, Joe Neeman, Praneeth Netrapalli
2016	J	`jnl`	Information-theoretic thresholds for community detection in sparse networks. CoRR Jess Banks, Cristopher Moore, Joe Neeman, Praneeth Netrapalli
2016	J	`jnl`	Learning Planar Ising Models. J. Mach. Learn. Res. Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli
2016	J	`jnl`	Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli
2016	J	`jnl`	Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. CoRR Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2016	J	`jnl`	Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. CoRR Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
2016	—	`conf`	Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. NIPS Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2016	J	`jnl`	Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. CoRR Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2016	A*	`conf`	Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. COLT Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford
2015	J	`jnl`	Computing Matrix Squareroot via Non Convex Local Search. CoRR Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli
2015	—	`conf`	Convergence Rates of Active Learning for Maximum Likelihood Estimation. NIPS Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
2015	J	`jnl`	Convergence Rates of Active Learning for Maximum Likelihood Estimation. CoRR Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
2015	A*	`conf`	Fast Exact Matrix Completion with Finite Samples. COLT Prateek Jain, Praneeth Netrapalli
2015	J	`jnl`	Phase Retrieval Using Alternating Minimization. IEEE Trans. Signal Process. Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
2015	J	`jnl`	Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. CoRR Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford
2014	J	`jnl`	Fast Exact Matrix Completion with Finite Samples. CoRR Prateek Jain, Praneeth Netrapalli
2014	A*	`conf`	Learning Sparsely Used Overcomplete Dictionaries. COLT Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
2014	B	`conf`	Learning structure of power-law Markov networks. ISIT Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath
2014	J	`jnl`	Non-Reconstructability in the Stochastic Block Model. CoRR Joe Neeman, Praneeth Netrapalli
2014	—	`conf`	Non-convex Robust PCA. NIPS Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain
2014	J	`jnl`	Non-convex Robust PCA. CoRR Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain
2013	J	`jnl`	Exact Recovery of Sparsely Used Overcomplete Dictionaries. CoRR Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
2013	J	`jnl`	Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. CoRR Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon
2013	A*	`conf`	Low-rank matrix completion using alternating minimization. STOC Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi
2013	—	`conf`	One-Bit Compressed Sensing: Provable Support and Vector Recovery. ICML (3) Sivakanth Gopi, Praneeth Netrapalli, Prateek Jain, Aditya V. Nori
2013	—	`conf`	Phase Retrieval using Alternating Minimization. NIPS Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
2013	J	`jnl`	Phase Retrieval using Alternating Minimization. CoRR Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
2012	J	`jnl`	Finding the Graph of Epidemic Cascades CoRR Praneeth Netrapalli, Sujay Sanghavi
2012	B	`conf`	Learning Markov graphs up to edit distance. ISIT Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath
2012	A*	`conf`	Learning the graph of epidemic cascades. SIGMETRICS Praneeth Netrapalli, Sujay Sanghavi
2012	J	`jnl`	Low-rank Matrix Completion using Alternating Minimization CoRR Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi
2010	—	`conf`	Greedy learning of Markov network structure. Allerton Praneeth Netrapalli, Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai
2010	J	`jnl`	Learning Planar Ising Models CoRR Jason K. Johnson, Praneeth Netrapalli, Michael Chertkov