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2020 – today
- 2024
[c61]Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan:
Second Order Methods for Bandit Optimization and Control. COLT 2024: 4691-4763- [c60]Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
Tandem Transformers for Inference Efficient LLMs. ICML 2024 - [i68]Aishwarya P. S., Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
Tandem Transformers for Inference Efficient LLMs. CoRR abs/2402.08644 (2024) - [i67]Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan:
Second Order Methods for Bandit Optimization and Control. CoRR abs/2402.08929 (2024) - [i66]Yashas Samaga, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli:
HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference. CoRR abs/2402.09360 (2024) - 2023
- [c59]Aniket Das, Dheeraj M. Nagaraj, Praneeth Netrapalli, Dheeraj Baby:
Near Optimal Heteroscedastic Regression with Symbiotic Learning. COLT 2023: 3696-3757 - [c58]Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain:
Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks. ICLR 2023 - [c57]Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain:
Multi-User Reinforcement Learning with Low Rank Rewards. ICML 2023: 25627-25659 - [c56]Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli:
Simplicity Bias in 1-Hidden Layer Neural Networks. NeurIPS 2023 - [c55]Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin:
Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. STOC 2023: 363-376 - [i65]Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli:
Simplicity Bias in 1-Hidden Layer Neural Networks. CoRR abs/2302.00457 (2023) - [i64]Dheeraj Baby, Aniket Das, Dheeraj Nagaraj, Praneeth Netrapalli:
Near Optimal Heteroscedastic Regression with Symbiotic Learning. CoRR abs/2306.14288 (2023) - [i63]Lénaïc Chizat, Praneeth Netrapalli:
Steering Deep Feature Learning with Backward Aligned Feature Updates. CoRR abs/2311.18718 (2023) - 2022
- [c54]Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Mysore Nagaraj, Praneeth Netrapalli:
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. ICLR 2022 - [c53]Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff:
Minimax Optimization with Smooth Algorithmic Adversaries. ICLR 2022 - [c52]Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi:
Focus on the Common Good: Group Distributional Robustness Follows. ICLR 2022 - [c51]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS 2022 - [i62]Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir:
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR abs/2202.04598 (2022) - [i61]Kushal Majmundar
, Sachin Goyal, Praneeth Netrapalli, Prateek Jain:
MET: Masked Encoding for Tabular Data. CoRR abs/2206.08564 (2022) - [i60]Ashwin Vaswani, Gaurav Aggarwal, Praneeth Netrapalli, Narayan G. Hegde:
All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP). CoRR abs/2206.08653 (2022) - [i59]Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain:
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. CoRR abs/2208.09139 (2022) - [i58]Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin:
Optimistic MLE - A Generic Model-based Algorithm for Partially Observable Sequential Decision Making. CoRR abs/2209.14997 (2022) - [i57]Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu, Praneeth Netrapalli, Prateek Jain:
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks. CoRR abs/2210.01360 (2022) - [i56]Naman Agarwal, Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Multi-User Reinforcement Learning with Low Rank Rewards. CoRR abs/2210.05355 (2022) - [i55]Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal:
Consistent Multiclass Algorithms for Complex Metrics and Constraints. CoRR abs/2210.09695 (2022) - 2021
- [j7]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points. J. ACM 68(2): 11:1-11:29 (2021) - [j6]Prateek Jain, Dheeraj M. Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. SIAM J. Optim. 31(2): 1108-1130 (2021) - [c50]Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli:
Efficient Bandit Convex Optimization: Beyond Linear Losses. COLT 2021: 4008-4067 - [c49]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML 2021: 9255-9264 - [c48]Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif:
No Quantum Speedup over Gradient Descent for Non-Smooth Convex Optimization. ITCS 2021: 53:1-53:20 - [c47]Harshay Shah, Prateek Jain, Praneeth Netrapalli:
Do Input Gradients Highlight Discriminative Features? NeurIPS 2021: 2046-2059 - [c46]Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli:
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. NeurIPS 2021: 8518-8531 - [c45]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Statistically and Computationally Efficient Linear Meta-representation Learning. NeurIPS 2021: 18487-18500 - [c44]Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif:
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness. NeurIPS 2021: 29874-29884 - [c43]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Streaming Linear System Identification with Reverse Experience Replay. NeurIPS 2021: 30140-30152 - [i54]Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain:
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR abs/2102.07387 (2021) - [i53]Harshay Shah, Prateek Jain, Praneeth Netrapalli:
Do Input Gradients Highlight Discriminative Features? CoRR abs/2102.12781 (2021) - [i52]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Streaming Linear System Identification with Reverse Experience Replay. CoRR abs/2103.05896 (2021) - [i51]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Sample Efficient Linear Meta-Learning by Alternating Minimization. CoRR abs/2105.08306 (2021) - [i50]Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. CoRR abs/2105.11558 (2021) - [i49]Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff:
Minimax Optimization with Smooth Algorithmic Adversaries. CoRR abs/2106.01488 (2021) - [i48]Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi:
Focus on the Common Good: Group Distributional Robustness Follows. CoRR abs/2110.02619 (2021) - [i47]Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. CoRR abs/2110.08440 (2021) - [i46]Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif:
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness. CoRR abs/2112.01118 (2021) - 2020
- [c42]Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar:
P-SIF: Document Embeddings Using Partition Averaging. AAAI 2020: 7863-7870 - [c41]Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford:
Leverage Score Sampling for Faster Accelerated Regression and ERM. ALT 2020: 22-47 - [c40]Arun Sai Suggala, Praneeth Netrapalli:
Online Non-Convex Learning: Following the Perturbed Leader is Optimal. ALT 2020: 845-861 - [c39]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML 2020: 4880-4889 - [c38]Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi:
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. ICML 2020: 7728-7738 - [c37]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS 2020 - [c36]Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli:
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. NeurIPS 2020 - [c35]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS 2020 - [c34]Arun Sai Suggala, Praneeth Netrapalli:
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. NeurIPS 2020 - [c33]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. NeurIPS 2020 - [i45]Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi:
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. CoRR abs/2003.12815 (2020) - [i44]Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims:
MOReL : Model-Based Offline Reinforcement Learning. CoRR abs/2005.05951 (2020) - [i43]Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar:
P-SIF: Document Embeddings Using Partition Averaging. CoRR abs/2005.09069 (2020) - [i42]Arun Sai Suggala, Praneeth Netrapalli:
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. CoRR abs/2006.07541 (2020) - [i41]Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli:
The Pitfalls of Simplicity Bias in Neural Networks. CoRR abs/2006.07710 (2020) - [i40]Guy Bresler, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Xian Wu:
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. CoRR abs/2006.08916 (2020) - [i39]Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar:
Learning Minimax Estimators via Online Learning. CoRR abs/2006.11430 (2020) - [i38]Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif:
No quantum speedup over gradient descent for non-smooth convex optimization. CoRR abs/2010.01801 (2020) - [i37]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. CoRR abs/2010.01848 (2020)
2010 – 2019
- 2019
- [c32]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. COLT 2019: 1752-1755 - [c31]Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli:
Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019: 3190-3193 - [c30]Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli:
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. ICML 2019: 4703-4711 - [c29]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. NeurIPS 2019: 12659-12670 - [c28]Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli:
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares. NeurIPS 2019: 14951-14962 - [i36]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR abs/1902.00618 (2019) - [i35]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm. CoRR abs/1902.03736 (2019) - [i34]Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan:
Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR abs/1902.04811 (2019) - [i33]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. CoRR abs/1903.01463 (2019) - [i32]Arun Sai Suggala, Praneeth Netrapalli:
Online Non-Convex Learning: Following the Perturbed Leader is Optimal. CoRR abs/1903.08110 (2019) - [i31]Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli:
Making the Last Iterate of SGD Information Theoretically Optimal. CoRR abs/1904.12443 (2019) - [i30]Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli:
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR abs/1904.12838 (2019) - [i29]Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh:
Efficient Algorithms for Smooth Minimax Optimization. CoRR abs/1907.01543 (2019) - [i28]Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli:
Non-Gaussianity of Stochastic Gradient Noise. CoRR abs/1910.09626 (2019) - 2018
- [c27]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT 2018: 545-604 - [c26]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. COLT 2018: 1042-1085 - [c25]Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. COLT 2018: 3243-3270 - [c24]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR 2018 - [c23]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ITCS 2018: 8:1-8:21 - [c22]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA 2018: 1-9 - [c21]Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli:
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. NeurIPS 2018: 10837-10847 - [i27]Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli:
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. CoRR abs/1803.00186 (2018) - [i26]Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade:
On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR abs/1803.05591 (2018) - 2017
- [j5]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. J. Mach. Learn. Res. 18: 223:1-223:42 (2017) - [j4]Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli:
A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Trans. Inf. Theory 63(1): 575-592 (2017) - [c20]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. AISTATS 2017: 479-488 - [c19]Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli:
Thresholding Based Outlier Robust PCA. COLT 2017: 593-628 - [c18]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). FSTTCS 2017: 2:1-2:10 - [c17]Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan:
How to Escape Saddle Points Efficiently. ICML 2017: 1724-1732 - [i25]Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli:
Thresholding based Efficient Outlier Robust PCA. CoRR abs/1702.05571 (2017) - [i24]Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan:
How to Escape Saddle Points Efficiently. CoRR abs/1703.00887 (2017) - [i23]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. CoRR abs/1704.04163 (2017) - [i22]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent. CoRR abs/1704.08227 (2017) - [i21]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). CoRR abs/1710.09430 (2017) - [i20]Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford:
Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR abs/1711.08426 (2017) - [i19]Chi Jin, Praneeth Netrapalli, Michael I. Jordan:
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. CoRR abs/1711.10456 (2017) - 2016
- [j3]Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli:
Learning Planar Ising Models. J. Mach. Learn. Res. 17: 215:1-215:26 (2016) - [j2]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli:
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. 26(4): 2775-2799 (2016) - [c16]Jess Banks, Cristopher Moore, Joe Neeman, Praneeth Netrapalli:
Information-theoretic thresholds for community detection in sparse networks. COLT 2016: 383-416 - [c15]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. COLT 2016: 1147-1164 - [c14]Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. ICML 2016: 2626-2634 - [c13]Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. ICML 2016: 2741-2750 - [c12]Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. NIPS 2016: 4520-4528 - [i18]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. CoRR abs/1602.06929 (2016) - [i17]Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. CoRR abs/1604.03930 (2016) - [i16]Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent. CoRR abs/1605.08370 (2016) - [i15]Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR abs/1605.08754 (2016) - [i14]Jess Banks, Cristopher Moore, Joe Neeman, Praneeth Netrapalli:
Information-theoretic thresholds for community detection in sparse networks. CoRR abs/1607.01760 (2016) - [i13]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. CoRR abs/1610.03774 (2016) - 2015
- [j1]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval Using Alternating Minimization. IEEE Trans. Signal Process. 63(18): 4814-4826 (2015) - [c11]Prateek Jain, Praneeth Netrapalli:
Fast Exact Matrix Completion with Finite Samples. COLT 2015: 1007-1034 - [c10]Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. NIPS 2015: 1090-1098 - [i12]Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi:
Convergence Rates of Active Learning for Maximum Likelihood Estimation. CoRR abs/1506.02348 (2015) - [i11]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli:
Computing Matrix Squareroot via Non Convex Local Search. CoRR abs/1507.05854 (2015) - [i10]Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. CoRR abs/1510.08896 (2015) - 2014
- [c9]Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon:
Learning Sparsely Used Overcomplete Dictionaries. COLT 2014: 123-137 - [c8]Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath:
Learning structure of power-law Markov networks. ISIT 2014: 2272-2276 - [c7]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. NIPS 2014: 1107-1115 - [i9]Joe Neeman, Praneeth Netrapalli:
Non-Reconstructability in the Stochastic Block Model. CoRR abs/1404.6304 (2014) - [i8]Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain:
Non-convex Robust PCA. CoRR abs/1410.7660 (2014) - [i7]Prateek Jain, Praneeth Netrapalli:
Fast Exact Matrix Completion with Finite Samples. CoRR abs/1411.1087 (2014) - 2013
- [c6]Sivakanth Gopi, Praneeth Netrapalli, Prateek Jain, Aditya V. Nori:
One-Bit Compressed Sensing: Provable Support and Vector Recovery. ICML (3) 2013: 154-162 - [c5]Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi:
Phase Retrieval using Alternating Minimization. NIPS 2013: 2796-2804 - [c4]