b'Elad Hazan'

research

∙ 07/21/2023

An Efficient Interior-Point Method for Online Convex Optimization

A new algorithm for regret minimization in online convex optimization is...

0 Elad Hazan, et al. ∙

research

∙ 06/14/2023

Online Learning for Obstacle Avoidance

We approach the fundamental problem of obstacle avoidance for robotic sy...

0 David Snyder, et al. ∙

research

∙ 05/27/2023

Online Nonstochastic Model-Free Reinforcement Learning

In this work, we explore robust model-free reinforcement learning algori...

0 Udaya Ghai, et al. ∙

research

∙ 05/24/2023

Optimal Rates for Bandit Nonstochastic Control

Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) con...

0 Y. Jennifer Sun, et al. ∙

research

∙ 02/07/2023

Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions

Adaptive regularization methods that exploit more than the diagonal entr...

1 Vladimir Feinberg, et al. ∙

research

∙ 01/19/2023

A Nonstochastic Control Approach to Optimization

Tuning optimizer hyperparameters, notably the learning rate to a particu...

0 Xinyi Chen, et al. ∙

research

∙ 11/22/2022

Projection-free Adaptive Regret with Membership Oracles

In the framework of online convex optimization, most iterative algorithm...

0 Zhou Lu, 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

∙ 11/17/2022

Introduction to Online Nonstochastic Control

This text presents an introduction to an emerging paradigm in control of...

0 Elad Hazan, et al. ∙

research

∙ 08/25/2022

Partial Matrix Completion

In the matrix completion problem, one wishes to reconstruct a low-rank m...

0 Varun Kanade, et al. ∙

research

∙ 07/01/2022

Efficient Adaptive Regret Minimization

In online convex optimization the player aims to minimize her regret aga...

0 Zhou Lu, et al. ∙

research

∙ 05/30/2022

Non-convex online learning via algorithmic equivalence

We study an algorithmic equivalence technique between nonconvex gradient...

0 Udaya Ghai, et al. ∙

research

∙ 03/02/2022

Adaptive Gradient Methods with Local Guarantees

Adaptive gradient methods are the method of choice for optimization in m...

16 Zhou Lu, et al. ∙

research

∙ 02/16/2022

Online Control of Unknown Time-Varying Dynamical Systems

We study online control of time-varying linear systems with unknown dyna...

0 Edgar Minasyan, et al. ∙

research

∙ 01/28/2022

A Regret Minimization Approach to Multi-Agent Control

We study the problem of multi-agent control of a dynamical system with k...

0 Udaya Ghai, 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

∙ 10/15/2021

Provable Regret Bounds for Deep Online Learning and Control

The use of deep neural networks has been highly successful in reinforcem...

0 Xinyi Chen, et al. ∙

research

∙ 08/22/2021

A Boosting Approach to Reinforcement Learning

We study efficient algorithms for reinforcement learning in Markov decis...

0 Nataly Brukhim, et al. ∙

research

∙ 07/16/2021

Robust Online Control with Model Misspecification

We study online control of an unknown nonlinear dynamical system that is...

0 Xinyi Chen, 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

∙ 02/18/2021

Boosting for Online Convex Optimization

We consider the decision-making framework of online convex optimization ...

0 Elad Hazan, 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

∙ 12/12/2020

Generating Adversarial Disturbances for Controller Verification

We consider the problem of generating maximally adversarial disturbances...

0 Udaya Ghai, et al. ∙

research

∙ 10/25/2020

Geometric Exploration for Online Control

We study the control of an unknown linear dynamical system under general...

6 Orestis Plevrakis, et al. ∙

research

∙ 08/12/2020

Non-Stochastic Control with Bandit Feedback

We study the problem of controlling a linear dynamical system with adver...

0 Paula Gradu, et al. ∙

research

∙ 07/23/2020

Online Boosting with Bandit Feedback

We consider the problem of online boosting for regression tasks, when on...

0 Nataly Brukhim, et al. ∙

research

∙ 07/13/2020

Black-Box Control for Linear Dynamical Systems

We consider the problem of controlling an unknown linear time-invariant ...

0 Xinyi Chen, et al. ∙

research

∙ 07/08/2020

Adaptive Regret for Control of Time-Varying Dynamics

We consider regret minimization for online control with time-varying lin...

0 Paula Gradu, et al. ∙

research

∙ 03/02/2020

Online Agnostic Boosting via Regret Minimization

Boosting is a widely used machine learning approach based on the idea of...

0 Nataly Brukhim, 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

∙ 01/31/2020

Boosting Simple Learners

We consider boosting algorithms under the restriction that the weak lear...

6 Noga Alon, et al. ∙

research

∙ 01/30/2020

Faster Projection-free Online Learning

In many online learning problems the computational bottleneck for gradie...

0 Elad Hazan, et al. ∙

research

∙ 01/25/2020

Improper Learning for Non-Stochastic Control

We consider the problem of controlling a possibly unknown linear dynamic...

0 Max Simchowitz, et al. ∙

research

∙ 11/27/2019

The Nonstochastic Control Problem

We consider the problem of controlling an unknown linear dynamical syste...

15 Elad Hazan, et al. ∙

research

∙ 11/06/2019

The gradient complexity of linear regression

We investigate the computational complexity of several basic linear alge...

14 Mark Braverman, 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

∙ 09/08/2019

Lecture Notes: Optimization for Machine Learning

Lecture notes on optimization for machine learning, derived from a cours...

0 Elad Hazan, et al. ∙

research

∙ 09/07/2019

Introduction to Online Convex Optimization

This manuscript portrays optimization as a process. In many practical ap...

0 Elad Hazan, 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

∙ 05/27/2019

Private Learning Implies Online Learning: An Efficient Reduction

We study the relationship between the notions of differentially private ...

0 Alon Gonen, 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

∙ 02/05/2019

Exponentiated Gradient Meets Gradient Descent

The (stochastic) gradient descent and the multiplicative update method a...

0 Udaya Ghai, et al. ∙

research

∙ 12/06/2018

Provably Efficient Maximum Entropy Exploration

Suppose an agent is in a (possibly unknown) Markov decision process (MDP...

2 Elad Hazan, et al. ∙

research

∙ 10/17/2018

Learning in Non-convex Games with an Optimization Oracle

We consider adversarial online learning in a non-convex setting under th...

0 Alon Gonen, 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

∙ 04/20/2018

Online Improper Learning with an Approximation Oracle

We revisit the question of reducing online learning to approximate optim...

0 Elad Hazan, et al. ∙

research

∙ 02/25/2018

Online Learning of Quantum States

Suppose we have many copies of an unknown n-qubit state ρ. We measure so...

0 Scott Aaronson, et al. ∙

research

∙ 02/19/2018

On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

Conventional wisdom in deep learning states that increasing depth improv...

0 Sanjeev Arora, et al. ∙

Elad Hazan

Featured Co-authors