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ORCIDNaman Agarwal
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2020 – today
- 2024
[c28]Naman Agarwal, James Pope
:
GenGradAttack: Efficient and Robust Targeted Adversarial Attacks Using Genetic Algorithms and Gradient-Based Fine-Tuning. ICAART (3) 2024: 202-209- [c27]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta:
Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements. ICML 2024 - [i38]Rudrajit Das, Naman Agarwal, Sujay Sanghavi, Inderjit S. Dhillon:
Towards Quantifying the Preconditioning Effect of Adam. CoRR abs/2402.07114 (2024) - [i37]Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao:
Stacking as Accelerated Gradient Descent. CoRR abs/2403.04978 (2024) - 2023
- [c26]Naman Agarwal, Brian Bullins, Karan Singh:
Variance-Reduced Conservative Policy Iteration. ALT 2023: 3-33 - [c25]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta:
Differentially Private and Lazy Online Convex Optimization. COLT 2023: 4599-4632 - [c24]Dheeraj Mysore Nagaraj, Suhas S. Kowshik, Naman Agarwal, Praneeth Netrapalli, Prateek Jain:
Multi-User Reinforcement Learning with Low Rank Rewards. ICML 2023: 25627-25659 - [c23]Naman Agarwal, Kyle O'Keefe, Richard Klukas:
Alternative Approach to Integrate GNSS Doppler in Kalman Filter for Smartphone Positioning. IPIN 2023: 1-6 - [c22]Gautam Goel, Naman Agarwal, Karan Singh, Elad Hazan:
Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret. L4DC 2023: 1345-1356 - [i36]George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson:
Benchmarking Neural Network Training Algorithms. CoRR abs/2306.07179 (2023) - [i35]Ankit Jha, Debabrata Pal, Mainak Singha, Naman Agarwal, Biplab Banerjee:
HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues. CoRR abs/2309.13470 (2023) - [i34]Naman Agarwal, Daniel Suo, Xinyi Chen, Elad Hazan:
Spectral State Space Models. CoRR abs/2312.06837 (2023) - [i33]Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Guha Thakurta:
Improved Differentially Private and Lazy Online Convex Optimization. CoRR abs/2312.11534 (2023) - 2022
- [c21]Naman Agarwal, Satyen Kale, Julian Zimmert:
Efficient Methods for Online Multiclass Logistic Regression. ALT 2022: 3-33 - [c20]Julian Zimmert, Naman Agarwal, Satyen Kale:
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. COLT 2022: 182-226 - [c19]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 - [i32]Julian Zimmert, Naman Agarwal, Satyen Kale:
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. CoRR abs/2202.02765 (2022) - [i31]Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E. Dahl, Justin Gilmer:
Adaptive Gradient Methods at the Edge of Stability. CoRR abs/2207.14484 (2022) - [i30]Naman Agarwal, Prateek Jain, Suhas S. Kowshik, Dheeraj Nagaraj, Praneeth Netrapalli:
Multi-User Reinforcement Learning with Low Rank Rewards. CoRR abs/2210.05355 (2022) - [i29]Gautam Goel, Naman Agarwal, Karan Singh, Elad Hazan:
Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret. CoRR abs/2211.11219 (2022) - [i28]Naman Agarwal, Brian Bullins, Karan Singh:
Variance-Reduced Conservative Policy Iteration. CoRR abs/2212.06283 (2022) - 2021
- [j5]Naman Agarwal, Nicolas Boumal, Brian Bullins, Coralia Cartis:
Adaptive regularization with cubics on manifolds. Math. Program. 188(1): 85-134 (2021) - [c18]Naman Agarwal, Pranjal Awasthi, Satyen Kale:
A Deep Conditioning Treatment of Neural Networks. ALT 2021: 249-305 - [c17]Naman Agarwal, Surbhi Goel, Cyril Zhang:
Acceleration via Fractal Learning Rate Schedules. ICML 2021: 87-99 - [c16]Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh:
A Regret Minimization Approach to Iterative Learning Control. ICML 2021: 100-109 - [c15]Naman Agarwal, Peter Kairouz, Ziyu Liu:
The Skellam Mechanism for Differentially Private Federated Learning. NeurIPS 2021: 5052-5064 - [i27]Daniel Suo, Cyril Zhang, Paula Gradu, Udaya Ghai, Xinyi Chen, Edgar Minasyan, Naman Agarwal, Karan Singh, Julienne LaChance, Tom Zajdel, Manuel Schottdorf, Daniel J. Cohen, Elad Hazan:
Machine Learning for Mechanical Ventilation Control. CoRR abs/2102.06779 (2021) - [i26]Paula Gradu, John Hallman, Daniel Suo, Alex Yu, Naman Agarwal, Udaya Ghai, Karan Singh, Cyril Zhang, Anirudha Majumdar, Elad Hazan:
Deluca - A Differentiable Control Library: Environments, Methods, and Benchmarking. CoRR abs/2102.09968 (2021) - [i25]Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh:
A Regret Minimization Approach to Iterative Learning Control. CoRR abs/2102.13478 (2021) - [i24]Naman Agarwal, Surbhi Goel, Cyril Zhang:
Acceleration via Fractal Learning Rate Schedules. CoRR abs/2103.01338 (2021) - [i23]Naman Agarwal, Satyen Kale, Julian Zimmert:
Efficient Methods for Online Multiclass Logistic Regression. CoRR abs/2110.03020 (2021) - [i22]Naman Agarwal, Peter Kairouz, Ziyu Liu:
The Skellam Mechanism for Differentially Private Federated Learning. CoRR abs/2110.04995 (2021) - [i21]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) - [i20]Daniel Suo, Cyril Zhang, Paula Gradu, Udaya Ghai, Xinyi Chen, Edgar Minasyan, Naman Agarwal, Karan Singh, Julienne LaChance, Tom Zajdel, Manuel Schottdorf, Daniel J. Cohen, Elad Hazan:
Machine Learning for Mechanical Ventilation Control (Extended Abstract). CoRR abs/2111.10434 (2021) - 2020
- [j4]Mamta Juneja, Shaswat Singh, Naman Agarwal, Shivank Bali, Shubham Gupta, Niharika Thakur, Prashant Jindal:
Automated detection of Glaucoma using deep learning convolution network (G-net). Multim. Tools Appl. 79(21-22): 15531-15553 (2020) - [c14]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 - [c13]Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang:
Extreme Tensoring for Low-Memory Preconditioning. ICLR 2020 - [c12]Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu:
Boosting for Control of Dynamical Systems. ICML 2020: 96-103 - [c11]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. NeurIPS 2020 - [i19]Naman Agarwal, Pranjal Awasthi, Satyen Kale:
A Deep Conditioning Treatment of Neural Networks. CoRR abs/2002.01523 (2020) - [i18]Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang:
Disentangling Adaptive Gradient Methods from Learning Rates. CoRR abs/2002.11803 (2020) - [i17]Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang:
Stochastic Optimization with Laggard Data Pipelines. CoRR abs/2010.13639 (2020)
2010 – 2019
- 2019
- [j3]Naman Agarwal, Karthekeyan Chandrasekaran, Alexandra Kolla, Vivek Madan:
On the Expansion of Group-Based Lifts. SIAM J. Discret. Math. 33(3): 1338-1373 (2019) - [c10]Naman Agarwal, Alon Gonen, Elad Hazan:
Learning in Non-convex Games with an Optimization Oracle. COLT 2019: 18-29 - [c9]Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang:
Efficient Full-Matrix Adaptive Regularization. ICML 2019: 102-110 - [c8]Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh:
Online Control with Adversarial Disturbances. ICML 2019: 111-119 - [c7]Naman Agarwal, Elad Hazan, Karan Singh:
Logarithmic Regret for Online Control. NeurIPS 2019: 10175-10184 - [i16]Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang:
Extreme Tensoring for Low-Memory Preconditioning. CoRR abs/1902.04620 (2019) - [i15]Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh:
Online Control with Adversarial Disturbances. CoRR abs/1902.08721 (2019) - [i14]Akash Jain, Manish Kumar, Rithvik Patibandla, Balamurugan R, Naveen Chandra R, Abhinav Arora, Akash K. Singh, Varun Pawar, Aditya Rai, Medha Agarwal, Priank Prasad, Vandit Sanadhya, Inshu Namdev, Nilay Shah, Saksham Mittal, Ayush Gupta, Naman Agarwal, Mangal Kothari:
Design and Development of Underwater Vehicle: ANAHITA. CoRR abs/1903.00494 (2019) - [i13]Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu:
Boosting for Dynamical Systems. CoRR abs/1906.08720 (2019) - [i12]Naman Agarwal, Elad Hazan, Karan Singh:
Logarithmic Regret for Online Control. CoRR abs/1909.05062 (2019) - 2018
- [b1]Naman Agarwal:
Second-Order Optimization Methods for Machine Learning. Princeton University, USA, 2018 - [c6]Naman Agarwal, Elad Hazan:
Lower Bounds for Higher-Order Convex Optimization. COLT 2018: 774-792 - [c5]Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, Brendan McMahan:
cpSGD: Communication-efficient and differentially-private distributed SGD. NeurIPS 2018: 7575-7586 - [i11]Naman Agarwal, Alon Gonen:
Effective Dimension of Exp-concave Optimization. CoRR abs/1805.08268 (2018) - [i10]Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, H. Brendan McMahan:
cpSGD: Communication-efficient and differentially-private distributed SGD. CoRR abs/1805.10559 (2018) - [i9]Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang:
The Case for Full-Matrix Adaptive Regularization. CoRR abs/1806.02958 (2018) - 2017
- [j2]Naman Agarwal, Brian Bullins, Elad Hazan:
Second-Order Stochastic Optimization for Machine Learning in Linear Time. J. Mach. Learn. Res. 18: 116:1-116:40 (2017) - [c4]Naman Agarwal, Karthekeyan Chandrasekaran, Alexandra Kolla, Vivek Madan:
On the Expansion of Group-Based Lifts. APPROX-RANDOM 2017: 24:1-24:13 - [c3]Naman Agarwal, Karan Singh:
The Price of Differential Privacy for Online Learning. ICML 2017: 32-40 - [c2]Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma:
Finding approximate local minima faster than gradient descent. STOC 2017: 1195-1199 - [i8]Naman Agarwal, Karan Singh:
The Price of Differential Privacy For Online Learning. CoRR abs/1701.07953 (2017) - [i7]Naman Agarwal, Elad Hazan:
Lower Bounds for Higher-Order Convex Optimization. CoRR abs/1710.10329 (2017) - [i6]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) - 2016
- [i5]Naman Agarwal, Brian Bullins, Elad Hazan:
Second Order Stochastic Optimization in Linear Time. CoRR abs/1602.03943 (2016) - [i4]Naman Agarwal, Zeyuan Allen Zhu, Brian Bullins, Elad Hazan, Tengyu Ma:
Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. CoRR abs/1611.01146 (2016) - 2015
- [j1]Naman Agarwal, Guy Kindler, Alexandra Kolla, Luca Trevisan:
Unique Games on the Hypercube. Chic. J. Theor. Comput. Sci. 2015 (2015) - [i3]Naman Agarwal, Afonso S. Bandeira, Konstantinos Koiliaris, Alexandra Kolla:
Multisection in the Stochastic Block Model using Semidefinite Programming. CoRR abs/1507.02323 (2015) - 2014
- [i2]Naman Agarwal, Guy Kindler, Alexandra Kolla, Luca Trevisan:
Unique Games on the Hypercube. CoRR abs/1405.1374 (2014) - 2013
- [i1]Naman Agarwal, Alexandra Kolla, Vivek Madan:
Small Lifts of Expander Graphs are Expanding. CoRR abs/1311.3268 (2013)
2000 – 2009
- 2007
- [c1]Ankit Sharma, Yatendra Kumar Singhal, Dhawal Makhija, Anuj Kumar Goyal, Naman Agarwal, Arti Bakhshi:
Factors Affecting e-Tailing Website Effectiveness: An Indian Perspective. ICIW 2007: 41
Coauthor Index
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