
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear ...
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InstanceWise MinimaxOptimal Algorithms for Logistic Bandits
Logistic Bandits have recently attracted substantial attention, by provi...
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RealTime Optimisation for Online Learning in Auctions
In display advertising, a small group of sellers and bidders face each o...
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Efficient Optimistic Exploration in LinearQuadratic Regulators via Lagrangian Relaxation
We study the explorationexploitation dilemma in the linear quadratic re...
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Improved Optimistic Algorithms for Logistic Bandits
The generalized linear bandit framework has attracted a lot of attention...
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Thompson Sampling in NonEpisodic Restless Bandits
Restless bandit problems assume timevarying reward distributions of the...
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Thresholding the virtual value: a simple method to increase welfare and lower reserve prices in online auction systems
Second price auctions with reserve price are widely used by the main Int...
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Explicit shading strategies for repeated truthful auctions
With the increasing use of auctions in online advertising, there has bee...
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Thompson Sampling for LinearQuadratic Control Problems
We consider the explorationexploitation tradeoff in linear quadratic (L...
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Linear Thompson Sampling Revisited
We derive an alternative proof for the regret of Thompson sampling () in...
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Marc Abeille
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