b'Naman Agarwal'

Acceleration via Fractal Learning Rate Schedules

When balancing the practical tradeoffs of iterative methods for large-sc...
03/01/2021 ∙ by Naman Agarwal, et al. ∙ 4 ∙ share

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A Regret Minimization Approach to Iterative Learning Control

We consider the setting of iterative learning control, or model-based po...
02/26/2021 ∙ by Naman Agarwal, et al. ∙ 11 ∙ share

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Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking

We present an open-source library of natively differentiable physics and...
02/19/2021 ∙ by Paula Gradu, et al. ∙ 30 ∙ share

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Machine Learning for Mechanical Ventilation Control

We consider the problem of controlling an invasive mechanical ventilator...
02/12/2021 ∙ by Daniel Suo, et al. ∙ 14 ∙ share

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Stochastic Optimization with Laggard Data Pipelines

State-of-the-art optimization is steadily shifting towards massively par...
10/26/2020 ∙ by Naman Agarwal, et al. ∙ 20 ∙ share

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Disentangling Adaptive Gradient Methods from Learning Rates

We investigate several confounding factors in the evaluation of optimiza...
02/26/2020 ∙ by Naman Agarwal, et al. ∙ 6 ∙ share

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A Deep Conditioning Treatment of Neural Networks

We study the role of depth in training randomly initialized overparamete...
02/04/2020 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Logarithmic Regret for Online Control

We study optimal regret bounds for control in linear dynamical systems u...
09/11/2019 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Boosting for Dynamical Systems

We propose a framework of boosting for learning and control in environme...
06/20/2019 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Design and Development of Underwater Vehicle: ANAHITA

Anahita is an autonomous underwater vehicle which is currently being dev...
03/01/2019 ∙ by Akash Jain, et al. ∙ 0 ∙ share

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Online Control with Adversarial Disturbances

We study the control of a linear dynamical system with adversarial distu...
02/23/2019 ∙ by Naman Agarwal, et al. ∙ 8 ∙ share

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Extreme Tensoring for Low-Memory Preconditioning

State-of-the-art models are now trained with billions of parameters, rea...
02/12/2019 ∙ by Xinyi Chen, et al. ∙ 0 ∙ share

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The Case for Full-Matrix Adaptive Regularization

Adaptive regularization methods come in diagonal and full-matrix variant...
06/08/2018 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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cpSGD: Communication-efficient and differentially-private distributed SGD

Distributed stochastic gradient descent is an important subroutine in di...
05/27/2018 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Effective Dimension of Exp-concave Optimization

We investigate the role of the effective (a.k.a. statistical) dimension ...
05/21/2018 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Leverage Score Sampling for Faster Accelerated Regression and ERM

Given a matrix A∈R^n× d and a vector b ∈R^d, we show how to compute an ϵ...
11/22/2017 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Lower Bounds for Higher-Order Convex Optimization

State-of-the-art methods in convex and non-convex optimization employ hi...
10/27/2017 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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The Price of Differential Privacy For Online Learning

We design differentially private algorithms for the problem of online li...
01/27/2017 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Finding Approximate Local Minima Faster than Gradient Descent

We design a non-convex second-order optimization algorithm that is guara...
11/03/2016 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Second-Order Stochastic Optimization for Machine Learning in Linear Time

First-order stochastic methods are the state-of-the-art in large-scale m...
02/12/2016 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Multisection in the Stochastic Block Model using Semidefinite Programming

We consider the problem of identifying underlying community-like structu...
07/08/2015 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Naman Agarwal

Featured Co-authors

Yi Zhang

Tengyu Ma

Sanjiv Kumar

Aaron Sidford

Sham M. Kakade

Elad Hazan

Praneeth Netrapalli

Yin Tat Lee

Zeyuan Allen-Zhu

Ananda Theertha Suresh

Sham Kakade

Acceleration via Fractal Learning Rate Schedules

A Regret Minimization Approach to Iterative Learning Control

Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking

Machine Learning for Mechanical Ventilation Control

Stochastic Optimization with Laggard Data Pipelines

Disentangling Adaptive Gradient Methods from Learning Rates

A Deep Conditioning Treatment of Neural Networks

Logarithmic Regret for Online Control

Boosting for Dynamical Systems

Design and Development of Underwater Vehicle: ANAHITA

Online Control with Adversarial Disturbances

Extreme Tensoring for Low-Memory Preconditioning

The Case for Full-Matrix Adaptive Regularization

cpSGD: Communication-efficient and differentially-private distributed SGD

Effective Dimension of Exp-concave Optimization

Leverage Score Sampling for Faster Accelerated Regression and ERM

Lower Bounds for Higher-Order Convex Optimization

The Price of Differential Privacy For Online Learning

Finding Approximate Local Minima Faster than Gradient Descent

Second-Order Stochastic Optimization for Machine Learning in Linear Time

Multisection in the Stochastic Block Model using Semidefinite Programming