Agarwal, Naman

29 publications

TMLR 2025 How Far Away Are Truly Hyperparameter-Free Learning Algorithms? Priya Kasimbeg, Vincent Roulet, Naman Agarwal, Sourabh Medapati, Fabian Pedregosa, Atish Agarwala, George E. Dahl

ICML 2025 Provable Length Generalization in Sequence Prediction via Spectral Filtering Annie Marsden, Evan Dogariu, Naman Agarwal, Xinyi Chen, Daniel Suo, Elad Hazan

ICML 2024 Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret Without Smoothness Requirements Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta

ICMLW 2024 Spectral State Space Models Naman Agarwal, Daniel Suo, Xinyi Chen, Elad Hazan

NeurIPSW 2023 Adaptive Gradient Methods at the Edge of Stability Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, Zachary Nado, George E. Dahl, Justin Gilmer

L4DC 2023 Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret Gautam Goel, Naman Agarwal, Karan Singh, Elad Hazan

COLT 2023 Differentially Private and Lazy Online Convex Optimization Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta

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

ALT 2023 Variance-Reduced Conservative Policy Iteration Naman Agarwal, Brian Bullins, Karan Singh

ALT 2022 Efficient Methods for Online Multiclass Logistic Regression Naman Agarwal, Satyen Kale, Julian Zimmert

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

COLT 2022 Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States Julian Zimmert, Naman Agarwal, Satyen Kale

ALT 2021 A Deep Conditioning Treatment of Neural Networks Naman Agarwal, Pranjal Awasthi, Satyen Kale

ICML 2021 A Regret Minimization Approach to Iterative Learning Control Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh

ICML 2021 Acceleration via Fractal Learning Rate Schedules Naman Agarwal, Surbhi Goel, Cyril Zhang

NeurIPS 2021 The Skellam Mechanism for Differentially Private Federated Learning Naman Agarwal, Peter Kairouz, Ziyu Liu

ICML 2020 Boosting for Control of Dynamical Systems Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu

ICLR 2020 Extreme Tensoring for Low-Memory Preconditioning Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang

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

ICLR 2020 Revisiting the Generalization of Adaptive Gradient Methods Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang

NeurIPS 2020 Stochastic Optimization with Laggard Data Pipelines Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang

ICML 2019 Efficient Full-Matrix Adaptive Regularization Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang

COLT 2019 Learning in Non-Convex Games with an Optimization Oracle Naman Agarwal, Alon Gonen, Elad Hazan

NeurIPS 2019 Logarithmic Regret for Online Control Naman Agarwal, Elad Hazan, Karan Singh

ICML 2019 Online Control with Adversarial Disturbances Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh

COLT 2018 Lower Bounds for Higher-Order Convex Optimization Naman Agarwal, Elad Hazan

NeurIPS 2018 cpSGD: Communication-Efficient and Differentially-Private Distributed SGD Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, Brendan McMahan

JMLR 2017 Second-Order Stochastic Optimization for Machine Learning in Linear Time Naman Agarwal, Brian Bullins, Elad Hazan

ICML 2017 The Price of Differential Privacy for Online Learning Naman Agarwal, Karan Singh