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Found 6 papers, 1 papers with code
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay
In this paper, we consider the problem of sequential change-point detection where both the change-points and the distributions before and after the change are assumed to be unknown.
Double-Linear Thompson Sampling for Context-Attentive Bandits
In this paper, we analyze and extend an online learning framework known as Context-Attentive Bandit, motivated by various practical applications, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration;however, the agent has a freedom to choose which variables to observe.
Decentralized Exploration in Multi-Armed Bandits -- Extended version
We consider the decentralized exploration problem: a set of players collaborate to identify the best arm by asynchronously interacting with the same stochastic environment.
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem
For the best-arm identification task, we introduce a version of Successive Elimination based on random shuffling of the $K$ arms.
Network of Bandits insure Privacy of end-users
We provide a first algorithm, Distributed Median Elimination, which is optimal in term of number of transmitted bits and near optimal in term of speed-up factor with respect to an optimal algorithm run independently on each player.
Random Forest for the Contextual Bandit Problem - extended version
The dependence of the sample complexity upon the number of contextual variables is logarithmic.
