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Found 5 papers, 0 papers with code
Online Learning with Feedback Graphs: The True Shape of Regret
Sequential learning with feedback graphs is a natural extension of the multi-armed bandit problem where the problem is equipped with an underlying graph structure that provides additional information - playing an action reveals the losses of all the neighbors of the action.
On the complexity of All $\varepsilon$-Best Arms Identification
Our method is based on a complete characterization of the alternative bandit instances that the optimal sampling strategy needs to rule out, thus making our bound tighter than the one provided by \cite{Mason2020}.
A Non-asymptotic Approach to Best-Arm Identification for Gaussian Bandits
We propose a new strategy for best-arm identification with fixed confidence of Gaussian variables with bounded means and unit variance.
Best Arm Identification in Spectral Bandits
We study best-arm identification with fixed confidence in bandit models with graph smoothness constraint.
Efficient learning by implicit exploration in bandit problems with side observations
As the predictions of our first algorithm cannot be always computed efficiently in this setting, we propose another algorithm with similar properties and with the benefit of always being computationally efficient, at the price of a slightly more complicated tuning mechanism.
