Praneeth Netrapalli

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 111

Top Venues

CoRR

NeurIPS

COLT

ICML

Publications

Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan
Second Order Methods for Bandit Optimization and Control. COLT (2024)
Arun Sai Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan
Second Order Methods for Bandit Optimization and Control. CoRR (2024)
Aishwarya P. S, Pranav Ajit Nair, Yashas Samaga, Toby Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli
Tandem Transformers for Inference Efficient LLMs. CoRR (2024)
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 (2024)
Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli
Simplicity Bias in 1-Hidden Layer Neural Networks. CoRR (2023)
Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain
Multi-User Reinforcement Learning with Low Rank Rewards. ICML (2023)
Aniket Das, Dheeraj M. Nagaraj, Praneeth Netrapalli, Dheeraj Baby
Near Optimal Heteroscedastic Regression with Symbiotic Learning. COLT (2023)
Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain
Feature Reconstruction From Outputs Can Mitigate Simplicity Bias in Neural Networks. ICLR (2023)
Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli
Simplicity Bias in 1-Hidden Layer Neural Networks. NeurIPS (2023)
Lénaïc Chizat, Praneeth Netrapalli
Steering Deep Feature Learning with Backward Aligned Feature Updates. CoRR (2023)
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making. STOC (2023)
Dheeraj Baby, Aniket Das, Dheeraj Nagaraj, Praneeth Netrapalli
Near Optimal Heteroscedastic Regression with Symbiotic Learning. CoRR (2023)
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári, Chi Jin
Optimistic MLE - A Generic Model-based Algorithm for Partially Observable Sequential Decision Making. CoRR (2022)
Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization. CoRR (2022)
Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
Focus on the Common Good: Group Distributional Robustness Follows. ICLR (2022)
Ashwin Vaswani, Gaurav Aggarwal, Praneeth Netrapalli, Narayan G. Hegde
All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP). CoRR (2022)
Naman Agarwal, Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Multi-User Reinforcement Learning with Low Rank Rewards. CoRR (2022)
Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu, Praneeth Netrapalli, Prateek Jain
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks. CoRR (2022)
Kushal Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain
MET: Masked Encoding for Tabular Data. CoRR (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. CoRR (2022)
Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
Minimax Optimization with Smooth Algorithmic Adversaries. ICLR (2022)
Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal
Consistent Multiclass Algorithms for Complex Metrics and Constraints. CoRR (2022)
Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I. Shamir
Reproducibility in Optimization: Theoretical Framework and Limits. NeurIPS (2022)
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)
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
Optimal regret algorithm for Pseudo-1d Bandit Convex Optimization. ICML (2021)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Statistically and Computationally Efficient Linear Meta-representation Learning. NeurIPS (2021)
Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
Focus on the Common Good: Group Distributional Robustness Follows. CoRR (2021)
Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Streaming Linear System Identification with Reverse Experience Replay. CoRR (2021)
Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
No Quantum Speedup over Gradient Descent for Non-Smooth Convex Optimization. ITCS (2021)
Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J. Ratliff
Minimax Optimization with Smooth Algorithmic Adversaries. CoRR (2021)
Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness. NeurIPS (2021)
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization. CoRR (2021)
Suhas S. Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. NeurIPS (2021)
Harshay Shah, Prateek Jain, Praneeth Netrapalli
Do Input Gradients Highlight Discriminative Features? CoRR (2021)
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) (2021)
Prateek Jain, Dheeraj M. Nagaraj, Praneeth Netrapalli
Making the Last Iterate of SGD Information Theoretically Optimal. SIAM J. Optim. 31 (2) (2021)
Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Streaming Linear System Identification with Reverse Experience Replay. NeurIPS (2021)
Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli
Efficient Bandit Convex Optimization: Beyond Linear Losses. COLT (2021)
Harshay Shah, Prateek Jain, Praneeth Netrapalli
Do Input Gradients Highlight Discriminative Features? NeurIPS (2021)
Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness. CoRR (2021)
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 (2021)
Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli
Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems. CoRR (2021)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Sample Efficient Linear Meta-Learning by Alternating Minimization. CoRR (2021)
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
The Pitfalls of Simplicity Bias in Neural Networks. NeurIPS (2020)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL: Model-Based Offline Reinforcement Learning. NeurIPS (2020)
Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif
No quantum speedup over gradient descent for non-smooth convex optimization. CoRR (2020)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. CoRR (2020)
Arun Sai Suggala, Praneeth Netrapalli
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. NeurIPS (2020)
Arun Sai Suggala, Praneeth Netrapalli
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games. CoRR (2020)
Arun Sai Suggala, Praneeth Netrapalli
Online Non-Convex Learning: Following the Perturbed Leader is Optimal. ALT (2020)
Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. NeurIPS (2020)
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? ICML (2020)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method. NeurIPS (2020)
Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. ICML (2020)
Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar
P-SIF: Document Embeddings Using Partition Averaging. AAAI (2020)
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)
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims
MOReL : Model-Based Offline Reinforcement Learning. CoRR (2020)
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli
The Pitfalls of Simplicity Bias in Neural Networks. CoRR (2020)
Guy Bresler, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli, Xian Wu
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms. CoRR (2020)
Kartik Gupta, Arun Sai Suggala, Adarsh Prasad, Praneeth Netrapalli, Pradeep Ravikumar
Learning Minimax Estimators via Online Learning. CoRR (2020)
Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar
P-SIF: Document Embeddings Using Partition Averaging. CoRR (2020)
Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition. CoRR (2020)
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)
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Making the Last Iterate of SGD Information Theoretically Optimal. CoRR (2019)
Arun Sai Suggala, Praneeth Netrapalli
Online Non-Convex Learning: Following the Perturbed Leader is Optimal. CoRR (2019)
Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli
Non-Gaussianity of Stochastic Gradient Noise. CoRR (2019)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Efficient Algorithms for Smooth Minimax Optimization. CoRR (2019)
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. CoRR (2019)
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 (2019)
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)
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh
Efficient Algorithms for Smooth Minimax Optimization. NeurIPS (2019)
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael I. Jordan
Stochastic Gradient Descent Escapes Saddle Points Efficiently. CoRR (2019)
Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure. CoRR (2019)
Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. ICML (2019)
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Making the Last Iterate of SGD Information Theoretically Optimal. COLT (2019)
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal. CoRR (2019)
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
On the insufficiency of existing momentum schemes for Stochastic Optimization. ICLR (2018)
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT (2018)
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
On the insufficiency of existing momentum schemes for Stochastic Optimization. CoRR (2018)
Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds. NeurIPS (2018)
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. COLT (2018)
Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. COLT (2018)
Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade
On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization. ITA (2018)
Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ITCS (2018)
Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form. CoRR (2018)
Chi Jin, Praneeth Netrapalli, Michael I. Jordan
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent. CoRR (2017)
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
How to Escape Saddle Points Efficiently. CoRR (2017)
Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. CoRR (2017)
Alekh Agarwal, Animashree Anandkumar, Praneeth Netrapalli
A Clustering Approach to Learning Sparsely Used Overcomplete Dictionaries. IEEE Trans. Inf. Theory 63 (1) (2017)
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)
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan
How to Escape Saddle Points Efficiently. ICML (2017)
Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot. AISTATS (2017)
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 (2017)
Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
Thresholding Based Outlier Robust PCA. COLT (2017)
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 (2017)
Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli
Thresholding based Efficient Outlier Robust PCA. CoRR (2017)
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford
Accelerating Stochastic Gradient Descent. CoRR (2017)
Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR (2017)
Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization. SIAM J. Optim. 26 (4) (2016)
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 (2016)

Praneeth Netrapalli.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Praneeth Netrapalli