Netrapalli, Praneeth

59 publications

NeurIPS 2025 Spark Transformer: Reactivating Sparsity in Transformer FFN and Attention 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 Levy, Sanjiv Kumar

JMLR 2024 Consistent Multiclass Algorithms for Complex Metrics and Constraints Harikrishna Narasimhan, Harish G. Ramaswamy, Shiv Kumar Tavker, Drona Khurana, Praneeth Netrapalli, Shivani Agarwal

COLT 2024 Second Order Methods for Bandit Optimization and Control Arun Suggala, Y Jennifer Sun, Praneeth Netrapalli, Elad Hazan

ICML 2024 Tandem Transformers for Inference Efficient LLMs P S Aishwarya, Pranav Ajit Nair, B L Yashas Samaga, Toby James Boyd, Sanjiv Kumar, Prateek Jain, Praneeth Netrapalli

NeurIPS 2024 The Feature Speed Formula: A Flexible Approach to Scale Hyper-Parameters of Deep Neural Networks Lénaïc Chizat, Praneeth Netrapalli

ICMLW 2024 Transformer Designs for In-Context Learning in Foundation Models for Time Series Forecasting with Covariates Afrin Dange, Vaibhav Raj, Praneeth Netrapalli, Sunita Sarawagi

ICLR 2023 Feature Reconstruction from Outputs Can Mitigate Simplicity Bias in Neural Networks Sravanti Addepalli, Anshul Nasery, Venkatesh Babu Radhakrishnan, Praneeth Netrapalli, Prateek Jain

ICML 2023 Multi-User Reinforcement Learning with Low Rank Rewards Dheeraj Mysore Nagaraj, Suhas S Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain

COLT 2023 Near Optimal Heteroscedastic Regression with Symbiotic Learning Aniket Das, Dheeraj M. Nagaraj, Praneeth Netrapalli, Dheeraj Baby

NeurIPS 2023 Simplicity Bias in 1-Hidden Layer Neural Networks Depen Morwani, Jatin Batra, Prateek Jain, Praneeth Netrapalli

ICMLW 2022 DAFT: Distilling Adversarially Fine-Tuned Teachers for OOD Robustness Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli, Prateek Jain

ICLR 2022 Focus on the Common Good: Group Distributional Robustness Follows Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi

NeurIPSW 2022 Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks Sravanti Addepalli, Anshul Nasery, Praneeth Netrapalli, Venkatesh Babu Radhakrishnan, Prateek Jain

NeurIPSW 2022 MET: Masked Encoding for Tabular Data Kushal Alpesh Majmundar, Sachin Goyal, Praneeth Netrapalli, Prateek Jain

ICLR 2022 Minimax Optimization with Smooth Algorithmic Adversaries Tanner Fiez, Chi Jin, Praneeth Netrapalli, Lillian J Ratliff

ICLR 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

NeurIPS 2022 Reproducibility in Optimization: Theoretical Framework and Limits Kwangjun Ahn, Prateek Jain, Ziwei Ji, Satyen Kale, Praneeth Netrapalli, Gil I Shamir

NeurIPS 2021 Do Input Gradients Highlight Discriminative Features? Harshay Shah, Prateek Jain, Praneeth Netrapalli

COLT 2021 Efficient Bandit Convex Optimization: Beyond Linear Losses Arun Sai Suggala, Pradeep Ravikumar, Praneeth Netrapalli

NeurIPS 2021 Near-Optimal Lower Bounds for Convex Optimization for All Orders of Smoothness Ankit Garg, Robin Kothari, Praneeth Netrapalli, Suhail Sherif

NeurIPS 2021 Near-Optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems Suhas Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli

ICML 2021 Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain

NeurIPS 2021 Statistically and Computationally Efficient Linear Meta-Representation Learning Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

NeurIPS 2021 Streaming Linear System Identification with Reverse Experience Replay Suhas Kowshik, Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli

ICML 2020 Efficient Domain Generalization via Common-Specific Low-Rank Decomposition Vihari Piratla, Praneeth Netrapalli, Sunita Sarawagi

NeurIPS 2020 Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games Arun Suggala, Praneeth Netrapalli

NeurIPS 2020 Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli

ALT 2020 Leverage Score Sampling for Faster Accelerated Regression and ERM Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin-Tat Lee, Praneeth Netrapalli, Aaron Sidford

NeurIPS 2020 MOReL: Model-Based Offline Reinforcement Learning Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

ALT 2020 Online Non-Convex Learning: Following the Perturbed Leader Is Optimal Arun Sai Suggala, Praneeth Netrapalli

AAAI 2020 P-SIF: Document Embeddings Using Partition Averaging Vivek Gupta, Ankit Saw, Pegah Nokhiz, Praneeth Netrapalli, Piyush Rai, Partha P. Talukdar

NeurIPS 2020 Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

NeurIPS 2020 The Pitfalls of Simplicity Bias in Neural Networks Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli

ICML 2020 What Is Local Optimality in Nonconvex-Nonconcave Minimax Optimization? Chi Jin, Praneeth Netrapalli, Michael Jordan

NeurIPS 2019 Efficient Algorithms for Smooth Minimax Optimization Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

COLT 2019 Making the Last Iterate of SGD Information Theoretically Optimal Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli

COLT 2019 Open Problem: Do Good Algorithms Necessarily Query Bad Points? Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli

ICML 2019 SGD Without Replacement: Sharper Rates for General Smooth Convex Functions Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli

NeurIPS 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

COLT 2018 Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent Chi Jin, Praneeth Netrapalli, Michael I. Jordan

COLT 2018 Accelerating Stochastic Gradient Descent for Least Squares Regression Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford

ICLR 2018 On the Insufficiency of Existing Momentum Schemes for Stochastic Optimization Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade

COLT 2018 Smoothed Analysis for Low-Rank Solutions to Semidefinite Programs in Quadratic Penalty Form Srinadh Bhojanapalli, Nicolas Boumal, Prateek Jain, Praneeth Netrapalli

NeurIPS 2018 Support Recovery for Orthogonal Matching Pursuit: Upper and Lower Bounds Raghav Somani, Chirag Gupta, Prateek Jain, Praneeth Netrapalli

AISTATS 2017 Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli

ICML 2017 How to Escape Saddle Points Efficiently Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael I. Jordan

COLT 2017 Thresholding Based Outlier Robust PCA Yeshwanth Cherapanamjeri, Prateek Jain, Praneeth Netrapalli

ICML 2016 Efficient Algorithms for Large-Scale Generalized Eigenvector Computation and Canonical Correlation Analysis Rong Ge, Chi Jin, Sham, Praneeth Netrapalli, Aaron Sidford

ICML 2016 Faster Eigenvector Computation via Shift-and-Invert Preconditioning Dan Garber, Elad Hazan, Chi Jin, Sham, Cameron Musco, Praneeth Netrapalli, Aaron Sidford

COLT 2016 Information-Theoretic Thresholds for Community Detection in Sparse Networks Jess Banks, Cristopher Moore, Joe Neeman, Praneeth Netrapalli

JMLR 2016 Learning Planar Ising Models Jason K. Johnson, Diane Oyen, Michael Chertkov, Praneeth Netrapalli

NeurIPS 2016 Provable Efficient Online Matrix Completion via Non-Convex Stochastic Gradient Descent Chi Jin, Sham M. Kakade, Praneeth Netrapalli

COLT 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

NeurIPS 2015 Convergence Rates of Active Learning for Maximum Likelihood Estimation Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi

COLT 2015 Fast Exact Matrix Completion with Finite Samples Prateek Jain, Praneeth Netrapalli

COLT 2014 Learning Sparsely Used Overcomplete Dictionaries Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli, Rashish Tandon

NeurIPS 2014 Non-Convex Robust PCA Praneeth Netrapalli, U N Niranjan, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain

ICML 2013 One-Bit Compressed Sensing: Provable Support and Vector Recovery Sivakant Gopi, Praneeth Netrapalli, Prateek Jain, Aditya Nori

NeurIPS 2013 Phase Retrieval Using Alternating Minimization Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi