Naman Agarwal

According to our database1, Naman Agarwal authored at least 50 papers between 2007 and 2024.

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Bibliography

2024
Stacking as Accelerated Gradient Descent.
CoRR, 2024

Towards Quantifying the Preconditioning Effect of Adam.
CoRR, 2024

GenGradAttack: Efficient and Robust Targeted Adversarial Attacks Using Genetic Algorithms and Gradient-Based Fine-Tuning.
Proceedings of the 16th International Conference on Agents and Artificial Intelligence, 2024

2023
Improved Differentially Private and Lazy Online Convex Optimization.
CoRR, 2023

Spectral State Space Models.
CoRR, 2023

HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues.
CoRR, 2023

Benchmarking Neural Network Training Algorithms.
CoRR, 2023

Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret.
Proceedings of the Learning for Dynamics and Control Conference, 2023

Alternative Approach to Integrate GNSS Doppler in Kalman Filter for Smartphone Positioning.
Proceedings of the 13th International Conference on Indoor Positioning and Indoor Navigation, 2023

Multi-User Reinforcement Learning with Low Rank Rewards.
Proceedings of the International Conference on Machine Learning, 2023

Differentially Private and Lazy Online Convex Optimization.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Variance-Reduced Conservative Policy Iteration.
Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022
Adaptive Gradient Methods at the Edge of Stability.
CoRR, 2022

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Efficient Methods for Online Multiclass Logistic Regression.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

2021
Adaptive regularization with cubics on manifolds.
Math. Program., 2021

Machine Learning for Mechanical Ventilation Control (Extended Abstract).
CoRR, 2021

Deluca - A Differentiable Control Library: Environments, Methods, and Benchmarking.
CoRR, 2021

Machine Learning for Mechanical Ventilation Control.
CoRR, 2021

The Skellam Mechanism for Differentially Private Federated Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Regret Minimization Approach to Iterative Learning Control.
Proceedings of the 38th International Conference on Machine Learning, 2021

Acceleration via Fractal Learning Rate Schedules.
Proceedings of the 38th International Conference on Machine Learning, 2021

A Deep Conditioning Treatment of Neural Networks.
Proceedings of the Algorithmic Learning Theory, 2021

2020
Automated detection of Glaucoma using deep learning convolution network (G-net).
Multim. Tools Appl., 2020

Disentangling Adaptive Gradient Methods from Learning Rates.
CoRR, 2020

Stochastic Optimization with Laggard Data Pipelines.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Boosting for Control of Dynamical Systems.
Proceedings of the 37th International Conference on Machine Learning, 2020

Extreme Tensoring for Low-Memory Preconditioning.
Proceedings of the 8th International Conference on Learning Representations, 2020

Leverage Score Sampling for Faster Accelerated Regression and ERM.
Proceedings of the Algorithmic Learning Theory, 2020

2019
On the Expansion of Group-Based Lifts.
SIAM J. Discret. Math., 2019

Boosting for Dynamical Systems.
CoRR, 2019

Design and Development of Underwater Vehicle: ANAHITA.
CoRR, 2019

Logarithmic Regret for Online Control.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Online Control with Adversarial Disturbances.
Proceedings of the 36th International Conference on Machine Learning, 2019

Efficient Full-Matrix Adaptive Regularization.
Proceedings of the 36th International Conference on Machine Learning, 2019

Learning in Non-convex Games with an Optimization Oracle.
Proceedings of the Conference on Learning Theory, 2019

2018
The Case for Full-Matrix Adaptive Regularization.
CoRR, 2018

Effective Dimension of Exp-concave Optimization.
CoRR, 2018

cpSGD: Communication-efficient and differentially-private distributed SGD.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Lower Bounds for Higher-Order Convex Optimization.
Proceedings of the Conference On Learning Theory, 2018

2017
Second-Order Stochastic Optimization for Machine Learning in Linear Time.
J. Mach. Learn. Res., 2017

Finding approximate local minima faster than gradient descent.
Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing, 2017

The Price of Differential Privacy for Online Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Finding Approximate Local Minima for Nonconvex Optimization in Linear Time.
CoRR, 2016

Second Order Stochastic Optimization in Linear Time.
CoRR, 2016

2015
Multisection in the Stochastic Block Model using Semidefinite Programming.
CoRR, 2015

Unique Games on the Hypercube.
Chic. J. Theor. Comput. Sci., 2015

2013
Small Lifts of Expander Graphs are Expanding.
CoRR, 2013

2007
Factors Affecting e-Tailing Website Effectiveness: An Indian Perspective.
Proceedings of the International Conference on Internet and Web Applications and Services (ICIW 2007), 2007


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