b'Naman Agarwal'

research

∙ 06/12/2023

Benchmarking Neural Network Training Algorithms

Training algorithms, broadly construed, are an essential part of every d...

6 George E. Dahl, et al. ∙

research

∙ 12/12/2022

Variance-Reduced Conservative Policy Iteration

We study the sample complexity of reducing reinforcement learning to a s...

0 Naman Agarwal, et al. ∙

research

∙ 11/21/2022

Best of Both Worlds in Online Control: Competitive Ratio and Policy Regret

We consider the fundamental problem of online control of a linear dynami...

0 Gautam Goel, et al. ∙

research

∙ 10/11/2022

Multi-User Reinforcement Learning with Low Rank Rewards

In this work, we consider the problem of collaborative multi-user reinfo...

16 Naman Agarwal, et al. ∙

research

∙ 07/29/2022

Adaptive Gradient Methods at the Edge of Stability

Very little is known about the training dynamics of adaptive gradient me...

30 Jeremy M Cohen, et al. ∙

research

∙ 02/06/2022

Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States

We revisit the classical online portfolio selection problem. It is widel...

2 Julian Zimmert, et al. ∙

research

∙ 11/19/2021

Machine Learning for Mechanical Ventilation Control (Extended Abstract)

Mechanical ventilation is one of the most widely used therapies in the I...

3 Daniel Suo, et al. ∙

research

∙ 10/16/2021

Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs

Q-learning is a popular Reinforcement Learning (RL) algorithm which is w...

5 Naman Agarwal, et al. ∙

research

∙ 10/11/2021

The Skellam Mechanism for Differentially Private Federated Learning

We introduce the multi-dimensional Skellam mechanism, a discrete differe...

15 Naman Agarwal, et al. ∙

research

∙ 10/06/2021

Efficient Methods for Online Multiclass Logistic Regression

Multiclass logistic regression is a fundamental task in machine learning...

0 Naman Agarwal, et al. ∙

research

∙ 03/01/2021

Acceleration via Fractal Learning Rate Schedules

When balancing the practical tradeoffs of iterative methods for large-sc...

4 Naman Agarwal, et al. ∙

research

∙ 02/26/2021

A Regret Minimization Approach to Iterative Learning Control

We consider the setting of iterative learning control, or model-based po...

11 Naman Agarwal, et al. ∙

research

∙ 02/19/2021

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

We present an open-source library of natively differentiable physics and...

30 Paula Gradu, et al. ∙

research

∙ 02/12/2021

Machine Learning for Mechanical Ventilation Control

We consider the problem of controlling an invasive mechanical ventilator...

14 Daniel Suo, et al. ∙

research

∙ 10/26/2020

Stochastic Optimization with Laggard Data Pipelines

State-of-the-art optimization is steadily shifting towards massively par...

20 Naman Agarwal, et al. ∙

research

∙ 02/26/2020

Disentangling Adaptive Gradient Methods from Learning Rates

We investigate several confounding factors in the evaluation of optimiza...

6 Naman Agarwal, et al. ∙

research

∙ 02/04/2020

A Deep Conditioning Treatment of Neural Networks

We study the role of depth in training randomly initialized overparamete...

0 Naman Agarwal, et al. ∙

research

∙ 09/11/2019

Logarithmic Regret for Online Control

We study optimal regret bounds for control in linear dynamical systems u...

0 Naman Agarwal, et al. ∙

research

∙ 06/20/2019

Boosting for Dynamical Systems

We propose a framework of boosting for learning and control in environme...

0 Naman Agarwal, et al. ∙

research

∙ 03/01/2019

Design and Development of Underwater Vehicle: ANAHITA

Anahita is an autonomous underwater vehicle which is currently being dev...

0 Akash Jain, et al. ∙

research

∙ 02/23/2019

Online Control with Adversarial Disturbances

We study the control of a linear dynamical system with adversarial distu...

8 Naman Agarwal, et al. ∙

research

∙ 02/12/2019

Extreme Tensoring for Low-Memory Preconditioning

State-of-the-art models are now trained with billions of parameters, rea...

0 Xinyi Chen, et al. ∙

research

∙ 06/08/2018

The Case for Full-Matrix Adaptive Regularization

Adaptive regularization methods come in diagonal and full-matrix variant...

0 Naman Agarwal, et al. ∙

research

∙ 05/27/2018

cpSGD: Communication-efficient and differentially-private distributed SGD

Distributed stochastic gradient descent is an important subroutine in di...

0 Naman Agarwal, et al. ∙

research

∙ 05/21/2018

Effective Dimension of Exp-concave Optimization

We investigate the role of the effective (a.k.a. statistical) dimension ...

0 Naman Agarwal, et al. ∙

research

∙ 11/22/2017

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...

0 Naman Agarwal, et al. ∙

research

∙ 10/27/2017

Lower Bounds for Higher-Order Convex Optimization

State-of-the-art methods in convex and non-convex optimization employ hi...

0 Naman Agarwal, et al. ∙

research

∙ 01/27/2017

The Price of Differential Privacy For Online Learning

We design differentially private algorithms for the problem of online li...

0 Naman Agarwal, et al. ∙

research

∙ 11/03/2016

Finding Approximate Local Minima Faster than Gradient Descent

We design a non-convex second-order optimization algorithm that is guara...

0 Naman Agarwal, et al. ∙

research

∙ 02/12/2016

Second-Order Stochastic Optimization for Machine Learning in Linear Time

First-order stochastic methods are the state-of-the-art in large-scale m...

0 Naman Agarwal, et al. ∙

research

∙ 07/08/2015

Multisection in the Stochastic Block Model using Semidefinite Programming

We consider the problem of identifying underlying community-like structu...

0 Naman Agarwal, et al. ∙

Naman Agarwal

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