Factors Affecting e-Tailing Website Effectiveness: An Indian Perspective. - Ankit Sharma 002; Yatendra Kumar Singhal; Dhawal Makhija; Anuj Kumar Goyal; Naman Agarwal; Arti Bakhshi (ICIW, 2007)

Small Lifts of Expander Graphs are Expanding. - Naman Agarwal; Alexandra Kolla; Vivek Madan (CoRR, 2013)

Unique Games on the Hypercube. - Naman Agarwal; Guy Kindler; Alexandra Kolla; Luca Trevisan 001 (Chic. J. Theor. Comput. Sci., 2015)

Multisection in the Stochastic Block Model using Semidefinite Programming. - Naman Agarwal; Afonso S. Bandeira; Konstantinos Koiliaris; Alexandra Kolla (CoRR, 2015)

Second Order Stochastic Optimization in Linear Time. - Naman Agarwal; Brian Bullins; Elad Hazan (CoRR, 2016)

Finding Approximate Local Minima for Nonconvex Optimization in Linear Time. - Naman Agarwal; Zeyuan Allen Zhu; Brian Bullins; Elad Hazan; Tengyu Ma 001 (CoRR, 2016)

The Price of Differential Privacy for Online Learning. - Naman Agarwal; Karan Singh (ICML, 2017)

Finding approximate local minima faster than gradient descent. - Naman Agarwal; Zeyuan Allen Zhu; Brian Bullins; Elad Hazan; Tengyu Ma 001 (STOC, 2017)

Second-Order Stochastic Optimization for Machine Learning in Linear Time. - Naman Agarwal; Brian Bullins; Elad Hazan (J. Mach. Learn. Res., 2017)

Lower Bounds for Higher-Order Convex Optimization. - Naman Agarwal; Elad Hazan (COLT, 2018)

cpSGD: Communication-efficient and differentially-private distributed SGD. - Naman Agarwal; Ananda Theertha Suresh; Felix X. Yu; Sanjiv Kumar; Brendan McMahan (NeurIPS, 2018)

Effective Dimension of Exp-concave Optimization. - Naman Agarwal; Alon Gonen (CoRR, 2018)

The Case for Full-Matrix Adaptive Regularization. - Naman Agarwal; Brian Bullins; Xinyi Chen 001; Elad Hazan; Karan Singh; Cyril Zhang; Yi Zhang 074 (CoRR, 2018)

Second-Order Optimization Methods for Machine Learning - Naman Agarwal (2018)

Learning in Non-convex Games with an Optimization Oracle. - Naman Agarwal; Alon Gonen; Elad Hazan (COLT, 2019)

Efficient Full-Matrix Adaptive Regularization. - Naman Agarwal; Brian Bullins; Xinyi Chen 001; Elad Hazan; Karan Singh; Cyril Zhang; Yi Zhang 074 (ICML, 2019)

Online Control with Adversarial Disturbances. - Naman Agarwal; Brian Bullins; Elad Hazan; Sham M. Kakade; Karan Singh (ICML, 2019)

Logarithmic Regret for Online Control. - Naman Agarwal; Elad Hazan; Karan Singh (NeurIPS, 2019)

Design and Development of Underwater Vehicle: ANAHITA. - 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; et al. (CoRR, 2019)

Boosting for Dynamical Systems. - Naman Agarwal; Nataly Brukhim; Elad Hazan; Zhou Lu (CoRR, 2019)

On the Expansion of Group-Based Lifts. - Naman Agarwal; Karthekeyan Chandrasekaran; Alexandra Kolla; Vivek Madan (SIAM J. Discret. Math., 2019)

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

Extreme Tensoring for Low-Memory Preconditioning. - Xinyi Chen 001; Naman Agarwal; Elad Hazan; Cyril Zhang; Yi Zhang 074 (ICLR, 2020)

Boosting for Control of Dynamical Systems. - Naman Agarwal; Nataly Brukhim; Elad Hazan; Zhou Lu (ICML, 2020)

Stochastic Optimization with Laggard Data Pipelines. - Naman Agarwal; Rohan Anil; Tomer Koren; Kunal Talwar; Cyril Zhang (NeurIPS, 2020)

Disentangling Adaptive Gradient Methods from Learning Rates. - Naman Agarwal; Rohan Anil; Elad Hazan; Tomer Koren; Cyril Zhang (CoRR, 2020)

Automated detection of Glaucoma using deep learning convolution network (G-net). - Mamta Juneja; Shaswat Singh; Naman Agarwal; Shivank Bali; Shubham Gupta; Niharika Thakur; Prashant Jindal (Multim. Tools Appl., 2020)

A Deep Conditioning Treatment of Neural Networks. - Naman Agarwal; Pranjal Awasthi; Satyen Kale (ALT, 2021)

Acceleration via Fractal Learning Rate Schedules. - Naman Agarwal; Surbhi Goel; Cyril Zhang (ICML, 2021)

A Regret Minimization Approach to Iterative Learning Control. - Naman Agarwal; Elad Hazan; Anirudha Majumdar; Karan Singh (ICML, 2021)

The Skellam Mechanism for Differentially Private Federated Learning. - Naman Agarwal; Peter Kairouz; Ziyu Liu 002 (NeurIPS, 2021)

Machine Learning for Mechanical Ventilation Control. - Daniel Suo; Cyril Zhang; Paula Gradu; Udaya Ghai; Xinyi Chen 001; Edgar Minasyan; Naman Agarwal; Karan Singh; Julienne LaChance; Tom Zajdel; Manuel Schottdorf; Daniel J. Cohen; et al. (CoRR, 2021)

Deluca - A Differentiable Control Library: Environments, Methods, and Benchmarking. - Paula Gradu; John Hallman; Daniel Suo; Alex Yu; Naman Agarwal; Udaya Ghai; Karan Singh; Cyril Zhang; Anirudha Majumdar; Elad Hazan (CoRR, 2021)

Machine Learning for Mechanical Ventilation Control (Extended Abstract). - Daniel Suo; Cyril Zhang; Paula Gradu; Udaya Ghai; Xinyi Chen 001; Edgar Minasyan; Naman Agarwal; Karan Singh; Julienne LaChance; Tom Zajdel; Manuel Schottdorf; Daniel J. Cohen; et al. (CoRR, 2021)

Adaptive regularization with cubics on manifolds. - Naman Agarwal; Nicolas Boumal; Brian Bullins; Coralia Cartis (Math. Program., 2021)

Efficient Methods for Online Multiclass Logistic Regression. - Naman Agarwal; Satyen Kale; Julian Zimmert (ALT, 2022)

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

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs. - Naman Agarwal; Syomantak Chaudhuri; Prateek Jain 002; Dheeraj Mysore Nagaraj; Praneeth Netrapalli (ICLR, 2022)

Adaptive Gradient Methods at the Edge of Stability. - Jeremy Cohen 001; Behrooz Ghorbani; Shankar Krishnan; Naman Agarwal; Sourabh Medapati; Michal Badura; Daniel Suo; David Cardoze; Zachary Nado; George E. Dahl; Justin Gilmer (CoRR, 2022)

Variance-Reduced Conservative Policy Iteration. - Naman Agarwal; Brian Bullins; Karan Singh (ALT, 2023)

Differentially Private and Lazy Online Convex Optimization. - Naman Agarwal; Satyen Kale; Karan Singh; Abhradeep Thakurta (COLT, 2023)

Multi-User Reinforcement Learning with Low Rank Rewards. - Dheeraj Mysore Nagaraj; Suhas S. Kowshik; Naman Agarwal; Praneeth Netrapalli; Prateek Jain 002 (ICML, 2023)

Alternative Approach to Integrate GNSS Doppler in Kalman Filter for Smartphone Positioning. - Naman Agarwal; Kyle O'Keefe; Richard Klukas (IPIN, 2023)

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

HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues. - Ankit Jha; Debabrata Pal; Mainak Singha; Naman Agarwal; Biplab Banerjee (PKDD/ECML Workshops (4), 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

Spectral State Space Models. - Naman Agarwal; Daniel Suo; Xinyi Chen 001; Elad Hazan (CoRR, 2023)

Improved Differentially Private and Lazy Online Convex Optimization. - Naman Agarwal; Satyen Kale; Karan Singh; Abhradeep Guha Thakurta (CoRR, 2023)

GenGradAttack: Efficient and Robust Targeted Adversarial Attacks Using Genetic Algorithms and Gradient-Based Fine-Tuning. - Naman Agarwal; James Pope (ICAART (3), 2024)

Improved Differentially Private and Lazy Online Convex Optimization: Lower Regret without Smoothness Requirements. - Naman Agarwal; Satyen Kale; Karan Singh; Abhradeep Guha Thakurta (ICML, 2024)

Towards Quantifying the Preconditioning Effect of Adam. - Rudrajit Das; Naman Agarwal; Sujay Sanghavi; Inderjit S. Dhillon (CoRR, 2024)

Stacking as Accelerated Gradient Descent. - Naman Agarwal; Pranjal Awasthi; Satyen Kale; Eric Zhao 003 (CoRR, 2024)

FutureFill: Fast Generation from Convolutional Sequence Models. - Naman Agarwal; Xinyi Chen 001; Evan Dogariu; Vladimir Feinberg; Daniel Suo; Peter L. Bartlett; Elad Hazan (CoRR, 2024)

Provable Length Generalization in Sequence Prediction via Spectral Filtering. - Annie Marsden; Evan Dogariu; Naman Agarwal; Xinyi Chen 001; Daniel Suo; Elad Hazan (CoRR, 2024)

Multivariate Time Series Data Mining for Failure Prediction & Root Cause Analysis. - Naman Agarwal; Gagan Raj Gupta; Vishwesh Jatala; Aniket Saha (ICASSP, 2025)

Cycle Slip repair for single-frequency smartphone GNSS using the Best Integer Equivariant estimator. - Naman Agarwal; Kyle O'Keefe (PLANS, 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 (Trans. Mach. Learn. Res., 2025)

Training neural networks faster with minimal tuning using pre-computed lists of hyperparameters for NAdamW. - Sourabh Medapati; Priya Kasimbeg; Shankar Krishnan; Naman Agarwal; George E. Dahl (CoRR, 2025)