Praneeth Netrapalli

According to our database, Praneeth Netrapalli authored at least 90 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference.

Yashas Samaga, Varun Yerram, Chong You, Srinadh Bhojanapalli, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli

CoRR, 2024

Second Order Methods for Bandit Optimization and Control.

Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan

CoRR, 2024

Tandem Transformers for Inference Efficient LLMs.

Aishwarya P. S, Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli

CoRR, 2024

2023

Steering Deep Feature Learning with Backward Aligned Feature Updates.

Lénaïc Chizat, Praneeth Netrapalli

CoRR, 2023

Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making.

Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Simplicity Bias in 1-Hidden Layer Neural Networks.

Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Multi-User Reinforcement Learning with Low Rank Rewards.

Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain

Proceedings of the International Conference on Machine Learning, 2023

Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks.

Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Near Optimal Heteroscedastic Regression with Symbiotic Learning.

Aniket Das, Dheeraj M. Nagaraj, Praneeth Netrapalli, Dheeraj Baby

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Consistent Multiclass Algorithms for Complex Metrics and Constraints.

Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal

CoRR, 2022

Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks.

Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu, Praneeth Netrapalli, Prateek Jain

CoRR, 2022

DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization.

Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain

CoRR, 2022

All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP).

Ashwin Vaswani, Gaurav Aggarwal, Praneeth Netrapalli, Narayan G. Hegde

CoRR, 2022

MET: Masked Encoding for Tabular Data.

Kushal Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain

CoRR, 2022

Reproducibility in Optimization: Theoretical Framework and Limits.

Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Focus on the Common Good: Group Distributional Robustness Follows.

Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi

Proceedings of the Tenth International Conference on Learning Representations, 2022

Minimax Optimization with Smooth Algorithmic Adversaries.

Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff

Proceedings of the Tenth International Conference on Learning Representations, 2022

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs.

Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Mysore Nagaraj, Praneeth Netrapalli

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Making the Last Iterate of SGD Information Theoretically Optimal.

Prateek Jain, Dheeraj M. Nagaraj, Praneeth Netrapalli

SIAM J. Optim., 2021

On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points.

Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan

J. ACM, 2021

Sample Efficient Linear Meta-Learning by Alternating Minimization.

Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

CoRR, 2021

Statistically and Computationally Efficient Linear Meta-representation Learning.

Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Do Input Gradients Highlight Discriminative Features?

Harshay Shah, Prateek Jain, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems.

Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Streaming Linear System Identification with Reverse Experience Replay.

Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness.

Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

No Quantum Speedup over Gradient Descent for Non-Smooth Convex Optimization.

Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif

Proceedings of the 12th Innovations in Theoretical Computer Science Conference, 2021

Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization.

Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain

Proceedings of the 38th International Conference on Machine Learning, 2021

Efficient Bandit Convex Optimization: Beyond Linear Losses.

Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli

Proceedings of the Conference on Learning Theory, 2021

2020

Learning Minimax Estimators via Online Learning.

Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar

CoRR, 2020

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method.

Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games.

Arun Sai Suggala, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The Pitfalls of Simplicity Bias in Neural Networks.

Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms.

Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

MOReL: Model-Based Offline Reinforcement Learning.

Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Domain Generalization via Common-Specific Low-Rank Decomposition.

Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi

Proceedings of the 37th International Conference on Machine Learning, 2020

What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Chi Jin, Praneeth Netrapalli, Michael I. Jordan

Proceedings of the 37th International Conference on Machine Learning, 2020

Online Non-Convex Learning: Following the Perturbed Leader is Optimal.

Arun Sai Suggala, Praneeth Netrapalli

Proceedings of the Algorithmic Learning Theory, 2020

Leverage Score Sampling for Faster Accelerated Regression and ERM.

Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford

Proceedings of the Algorithmic Learning Theory, 2020

P-SIF: Document Embeddings Using Partition Averaging.

Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Non-Gaussianity of Stochastic Gradient Noise.

Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli

CoRR, 2019

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure.

Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

CoRR, 2019

Stochastic Gradient Descent Escapes Saddle Points Efficiently.

Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan

CoRR, 2019

A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm.

Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan

CoRR, 2019

Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal.

Chi Jin, Praneeth Netrapalli, Michael I. Jordan

CoRR, 2019

Efficient Algorithms for Smooth Minimax Optimization.

Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares.

Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

SGD without Replacement: Sharper Rates for General Smooth Convex Functions.

Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli

Proceedings of the 36th International Conference on Machine Learning, 2019

Open Problem: Do Good Algorithms Necessarily Query Bad Points?

Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli

Proceedings of the Conference on Learning Theory, 2019

2018

Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds.

Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization.

Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade

Proceedings of the 2018 Information Theory and Applications Workshop, 2018

Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness.

Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff

Proceedings of the 9th Innovations in Theoretical Computer Science Conference, 2018

Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent.

Chi Jin, Praneeth Netrapalli, Michael I. Jordan

Proceedings of the Conference On Learning Theory, 2018

Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form.

Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli

Proceedings of the Conference On Learning Theory, 2018

Accelerating Stochastic Gradient Descent for Least Squares Regression.

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

Proceedings of the Conference On Learning Theory, 2018

2017

A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli

IEEE Trans. Inf. Theory, 2017

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification.

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

J. Mach. Learn. Res., 2017

Thresholding based Efficient Outlier Robust PCA.

Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli

CoRR, 2017

Accelerating Stochastic Gradient Descent.

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

CoRR, 2017

How to Escape Saddle Points Efficiently.

Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan

Proceedings of the 34th International Conference on Machine Learning, 2017

A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares).

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford

Proceedings of the 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, 2017

Thresholding Based Outlier Robust PCA.

Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli

Proceedings of the 30th Conference on Learning Theory, 2017

Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot.

Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli

SIAM J. Optim., 2016

Learning Planar Ising Models.

Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli

J. Mach. Learn. Res., 2016

Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging.

Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

CoRR, 2016

Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm.

Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford

CoRR, 2016

Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent.

Chi Jin, Sham M. Kakade, Praneeth Netrapalli

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis.

Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford

Proceedings of the 33nd International Conference on Machine Learning, 2016

Faster Eigenvector Computation via Shift-and-Invert Preconditioning.

Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford

Proceedings of the 33nd International Conference on Machine Learning, 2016

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm.

Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford

Proceedings of the 29th Conference on Learning Theory, 2016

Information-theoretic thresholds for community detection in sparse networks.

Jess Banks, Cristopher Moore, Joe Neeman, Praneeth Netrapalli

Proceedings of the 29th Conference on Learning Theory, 2016

2015

Phase Retrieval Using Alternating Minimization.

Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi

IEEE Trans. Signal Process., 2015

Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation.

Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford

CoRR, 2015

Computing Matrix Squareroot via Non Convex Local Search.

Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli

CoRR, 2015

Convergence Rates of Active Learning for Maximum Likelihood Estimation.

Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Exact Matrix Completion with Finite Samples.

Prateek Jain, Praneeth Netrapalli

Proceedings of The 28th Conference on Learning Theory, 2015

2014

Non-Reconstructability in the Stochastic Block Model.

Joe Neeman, Praneeth Netrapalli

CoRR, 2014

Non-convex Robust PCA.

Praneeth Netrapalli, U. N. Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Learning structure of power-law Markov networks.

Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath

Proceedings of the 2014 IEEE International Symposium on Information Theory, Honolulu, HI, USA, June 29, 2014

Learning Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

Proceedings of The 27th Conference on Learning Theory, 2014

2013

Exact Recovery of Sparsely Used Overcomplete Dictionaries.

Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli

CoRR, 2013

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization.

Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

CoRR, 2013

Low-rank matrix completion using alternating minimization.

Prateek Jain, Praneeth Netrapalli, Sujay Sanghavi

Proceedings of the Symposium on Theory of Computing Conference, 2013

One-Bit Compressed Sensing: Provable Support and Vector Recovery.

Sivakanth Gopi, Praneeth Netrapalli, Prateek Jain, Aditya V. Nori

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Finding the Graph of Epidemic Cascades

Praneeth Netrapalli, Sujay Sanghavi

CoRR, 2012

Learning the graph of epidemic cascades.

Praneeth Netrapalli, Sujay Sanghavi

Proceedings of the ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, 2012

Learning Markov graphs up to edit distance.

Abhik Kumar Das, Praneeth Netrapalli, Sujay Sanghavi, Sriram Vishwanath

Proceedings of the 2012 IEEE International Symposium on Information Theory, 2012

2010

Learning Planar Ising Models

Jason K. Johnson, Praneeth Netrapalli, Michael Chertkov

CoRR, 2010

Greedy learning of Markov network structure.

Praneeth Netrapalli, Siddhartha Banerjee, Sujay Sanghavi, Sanjay Shakkottai

Proceedings of the 48th Annual Allerton Conference on Communication, 2010