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The Nonstochastic Control Problem
We consider the problem of controlling an unknown linear dynamical syste...
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Improper Learning for Non-Stochastic Control
We consider the problem of controlling a possibly unknown linear dynamic...
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Bandit Linear Control
We consider the problem of controlling a known linear dynamical system u...
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Geometric Exploration for Online Control
We study the control of an unknown linear dynamical system under general...
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Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
We consider the problem of bandit optimization, inspired by stochastic o...
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Semi-bandit Optimization in the Dispersed Setting
In this work, we study the problem of online optimization of piecewise L...
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Setpoint Tracking with Partially Observed Loads
We use online convex optimization (OCO) for setpoint tracking with uncer...
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Non-Stochastic Control with Bandit Feedback
We study the problem of controlling a linear dynamical system with adversarial perturbations where the only feedback available to the controller is the scalar loss, and the loss function itself is unknown. For this problem, with either a known or unknown system, we give an efficient sublinear regret algorithm. The main algorithmic difficulty is the dependence of the loss on past controls. To overcome this issue, we propose an efficient algorithm for the general setting of bandit convex optimization for loss functions with memory, which may be of independent interest.
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