Search Results for author: Naman Agarwal

Found 25 papers, 5 papers with code

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

no code implementations16 Oct 2021 Naman Agarwal, Syomantak Chaudhuri, Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli

The starting point of our work is the observation that in practice, Q-learning is used with two important modifications: (i) training with two networks, called online network and target network simultaneously (online target learning, or OTL) , and (ii) experience replay (ER) (Mnih et al., 2015).


The Skellam Mechanism for Differentially Private Federated Learning

no code implementations11 Oct 2021 Naman Agarwal, Peter Kairouz, Ziyu Liu

We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy mechanism based on the difference of two independent Poisson random variables.

Federated Learning

Efficient Methods for Online Multiclass Logistic Regression

no code implementations6 Oct 2021 Naman Agarwal, Satyen Kale, Julian Zimmert

Previous work (Foster et al., 2018) has highlighted the importance of improper predictors for achieving "fast rates" in the online multiclass logistic regression problem without suffering exponentially from secondary problem parameters, such as the norm of the predictors in the comparison class.

Acceleration via Fractal Learning Rate Schedules

1 code implementation1 Mar 2021 Naman Agarwal, Surbhi Goel, Cyril Zhang

In practical applications of iterative first-order optimization, the learning rate schedule remains notoriously difficult to understand and expensive to tune.

A Regret Minimization Approach to Iterative Learning Control

no code implementations26 Feb 2021 Naman Agarwal, Elad Hazan, Anirudha Majumdar, Karan Singh

We consider the setting of iterative learning control, or model-based policy learning in the presence of uncertain, time-varying dynamics.

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

1 code implementation19 Feb 2021 Paula Gradu, John Hallman, Daniel Suo, Alex Yu, Naman Agarwal, Udaya Ghai, Karan Singh, Cyril Zhang, Anirudha Majumdar, Elad Hazan

We present an open-source library of natively differentiable physics and robotics environments, accompanied by gradient-based control methods and a benchmark-ing suite.

OpenAI Gym

Machine Learning for Mechanical Ventilation Control

no code implementations12 Feb 2021 Daniel Suo, Cyril Zhang, Paula Gradu, Udaya Ghai, Xinyi Chen, Edgar Minasyan, Naman Agarwal, Karan Singh, Julienne LaChance, Tom Zajdel, Manuel Schottdorf, Daniel Cohen, Elad Hazan

We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.

Stochastic Optimization with Laggard Data Pipelines

no code implementations NeurIPS 2020 Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang

State-of-the-art optimization is steadily shifting towards massively parallel pipelines with extremely large batch sizes.

Stochastic Optimization

Disentangling Adaptive Gradient Methods from Learning Rates

1 code implementation26 Feb 2020 Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang

We investigate several confounding factors in the evaluation of optimization algorithms for deep learning.

A Deep Conditioning Treatment of Neural Networks

no code implementations4 Feb 2020 Naman Agarwal, Pranjal Awasthi, Satyen Kale

We study the role of depth in training randomly initialized overparameterized neural networks.

Revisiting the Generalization of Adaptive Gradient Methods

no code implementations ICLR 2020 Naman Agarwal, Rohan Anil, Elad Hazan, Tomer Koren, Cyril Zhang

A commonplace belief in the machine learning community is that using adaptive gradient methods hurts generalization.

Logarithmic Regret for Online Control

no code implementations NeurIPS 2019 Naman Agarwal, Elad Hazan, Karan Singh

We study optimal regret bounds for control in linear dynamical systems under adversarially changing strongly convex cost functions, given the knowledge of transition dynamics.

Boosting for Control of Dynamical Systems

no code implementations ICML 2020 Naman Agarwal, Nataly Brukhim, Elad Hazan, Zhou Lu

We study the question of how to aggregate controllers for dynamical systems in order to improve their performance.

Online Control with Adversarial Disturbances

no code implementations23 Feb 2019 Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh

We study the control of a linear dynamical system with adversarial disturbances (as opposed to statistical noise).

Extreme Tensoring for Low-Memory Preconditioning

no code implementations ICLR 2020 Xinyi Chen, Naman Agarwal, Elad Hazan, Cyril Zhang, Yi Zhang

State-of-the-art models are now trained with billions of parameters, reaching hardware limits in terms of memory consumption.

Stochastic Optimization

Learning in Non-convex Games with an Optimization Oracle

no code implementations17 Oct 2018 Naman Agarwal, Alon Gonen, Elad Hazan

We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle.

Efficient Full-Matrix Adaptive Regularization

no code implementations ICLR 2019 Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang

Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive.

Optimal Sketching Bounds for Exp-concave Stochastic Minimization

no code implementations21 May 2018 Naman Agarwal, Alon Gonen

We derive optimal statistical and computational complexity bounds for exp-concave stochastic minimization in terms of the effective dimension.

Leverage Score Sampling for Faster Accelerated Regression and ERM

no code implementations22 Nov 2017 Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford

Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a vector $b \in\mathbb{R}^{d}$, we show how to compute an $\epsilon$-approximate solution to the regression problem $ \min_{x\in\mathbb{R}^{d}}\frac{1}{2} \|\mathbf{A} x - b\|_{2}^{2} $ in time $ \tilde{O} ((n+\sqrt{d\cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1}) $ where $\kappa_{\text{sum}}=\mathrm{tr}\left(\mathbf{A}^{\top}\mathbf{A}\right)/\lambda_{\min}(\mathbf{A}^{T}\mathbf{A})$ and $s$ is the maximum number of non-zero entries in a row of $\mathbf{A}$.

Lower Bounds for Higher-Order Convex Optimization

no code implementations27 Oct 2017 Naman Agarwal, Elad Hazan

State-of-the-art methods in convex and non-convex optimization employ higher-order derivative information, either implicitly or explicitly.

The Price of Differential Privacy For Online Learning

no code implementations ICML 2017 Naman Agarwal, Karan Singh

We design differentially private algorithms for the problem of online linear optimization in the full information and bandit settings with optimal $\tilde{O}(\sqrt{T})$ regret bounds.

Multi-Armed Bandits

Finding Approximate Local Minima Faster than Gradient Descent

1 code implementation3 Nov 2016 Naman Agarwal, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan, Tengyu Ma

We design a non-convex second-order optimization algorithm that is guaranteed to return an approximate local minimum in time which scales linearly in the underlying dimension and the number of training examples.

Second-Order Stochastic Optimization for Machine Learning in Linear Time

2 code implementations12 Feb 2016 Naman Agarwal, Brian Bullins, Elad Hazan

First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity.

Stochastic Optimization

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