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07/21/2023
An Efficient Interior-Point Method for Online Convex Optimization
A new algorithm for regret minimization in online convex optimization is...
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06/14/2023
Online Learning for Obstacle Avoidance
We approach the fundamental problem of obstacle avoidance for robotic sy...
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05/27/2023
Online Nonstochastic Model-Free Reinforcement Learning
In this work, we explore robust model-free reinforcement learning algori...
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05/24/2023
Optimal Rates for Bandit Nonstochastic Control
Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) con...
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02/07/2023
Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions
Adaptive regularization methods that exploit more than the diagonal entr...
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01/19/2023
A Nonstochastic Control Approach to Optimization
Tuning optimizer hyperparameters, notably the learning rate to a particu...
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11/22/2022
Projection-free Adaptive Regret with Membership Oracles
In the framework of online convex optimization, most iterative algorithm...
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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...
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11/17/2022
Introduction to Online Nonstochastic Control
This text presents an introduction to an emerging paradigm in control of...
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08/25/2022
Partial Matrix Completion
In the matrix completion problem, one wishes to reconstruct a low-rank m...
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07/01/2022
Efficient Adaptive Regret Minimization
In online convex optimization the player aims to minimize her regret aga...
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05/30/2022
Non-convex online learning via algorithmic equivalence
We study an algorithmic equivalence technique between nonconvex gradient...
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03/02/2022
Adaptive Gradient Methods with Local Guarantees
Adaptive gradient methods are the method of choice for optimization in m...
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02/16/2022
Online Control of Unknown Time-Varying Dynamical Systems
We study online control of time-varying linear systems with unknown dyna...
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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...
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11/19/2021
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the I...
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10/15/2021
Provable Regret Bounds for Deep Online Learning and Control
The use of deep neural networks has been highly successful in reinforcem...
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08/22/2021
A Boosting Approach to Reinforcement Learning
We study efficient algorithms for reinforcement learning in Markov decis...
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07/16/2021
Robust Online Control with Model Misspecification
We study online control of an unknown nonlinear dynamical system that is...
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02/26/2021
A Regret Minimization Approach to Iterative Learning Control
We consider the setting of iterative learning control, or model-based po...
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02/19/2021
Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking
We present an open-source library of natively differentiable physics and...
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02/18/2021
Boosting for Online Convex Optimization
We consider the decision-making framework of online convex optimization ...
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02/12/2021
Machine Learning for Mechanical Ventilation Control
We consider the problem of controlling an invasive mechanical ventilator...
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12/12/2020
Generating Adversarial Disturbances for Controller Verification
We consider the problem of generating maximally adversarial disturbances...
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10/25/2020
Geometric Exploration for Online Control
We study the control of an unknown linear dynamical system under general...
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08/12/2020
Non-Stochastic Control with Bandit Feedback
We study the problem of controlling a linear dynamical system with adver...
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07/23/2020
Online Boosting with Bandit Feedback
We consider the problem of online boosting for regression tasks, when on...
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07/13/2020
Black-Box Control for Linear Dynamical Systems
We consider the problem of controlling an unknown linear time-invariant ...
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07/08/2020
Adaptive Regret for Control of Time-Varying Dynamics
We consider regret minimization for online control with time-varying lin...
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03/02/2020
Online Agnostic Boosting via Regret Minimization
Boosting is a widely used machine learning approach based on the idea of...
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02/26/2020
Disentangling Adaptive Gradient Methods from Learning Rates
We investigate several confounding factors in the evaluation of optimiza...
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01/31/2020
Boosting Simple Learners
We consider boosting algorithms under the restriction that the weak lear...
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01/30/2020
Faster Projection-free Online Learning
In many online learning problems the computational bottleneck for gradie...
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01/25/2020
Improper Learning for Non-Stochastic Control
We consider the problem of controlling a possibly unknown linear dynamic...
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11/27/2019
The Nonstochastic Control Problem
We consider the problem of controlling an unknown linear dynamical syste...
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11/06/2019
The gradient complexity of linear regression
We investigate the computational complexity of several basic linear alge...
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09/11/2019
Logarithmic Regret for Online Control
We study optimal regret bounds for control in linear dynamical systems u...
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09/08/2019
Lecture Notes: Optimization for Machine Learning
Lecture notes on optimization for machine learning, derived from a cours...
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09/07/2019
Introduction to Online Convex Optimization
This manuscript portrays optimization as a process. In many practical ap...
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06/20/2019
Boosting for Dynamical Systems
We propose a framework of boosting for learning and control in environme...
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05/27/2019
Private Learning Implies Online Learning: An Efficient Reduction
We study the relationship between the notions of differentially private ...
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02/23/2019
Online Control with Adversarial Disturbances
We study the control of a linear dynamical system with adversarial distu...
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02/12/2019
Extreme Tensoring for Low-Memory Preconditioning
State-of-the-art models are now trained with billions of parameters, rea...
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02/05/2019
Exponentiated Gradient Meets Gradient Descent
The (stochastic) gradient descent and the multiplicative update method a...
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12/06/2018
Provably Efficient Maximum Entropy Exploration
Suppose an agent is in a (possibly unknown) Markov decision process (MDP...
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10/17/2018
Learning in Non-convex Games with an Optimization Oracle
We consider adversarial online learning in a non-convex setting under th...
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06/08/2018
The Case for Full-Matrix Adaptive Regularization
Adaptive regularization methods come in diagonal and full-matrix variant...
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04/20/2018
Online Improper Learning with an Approximation Oracle
We revisit the question of reducing online learning to approximate optim...
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02/25/2018
Online Learning of Quantum States
Suppose we have many copies of an unknown n-qubit state ρ. We measure so...
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02/19/2018
